forum_id string | forum_title string | forum_authors list | forum_abstract string | forum_keywords list | forum_pdf_url string | forum_url string | note_id string | note_type string | note_created int64 | note_replyto string | note_readers list | note_signatures list | venue string | year string | note_text string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
XCUzATsVdU | One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning | [
"Dhruv Jain",
"Tsiry Mayet",
"Romain HÉRAULT",
"Romain MODZELEWSKI"
] | Recent studies on contrastive learning have emphasized carefully sampling and mixing negative samples.
This study introduces a novel and improved approach for generating synthetic negatives.
We propose a new method using One-Class Support Vector Machine (OCSVM) to guide in the selection process before mixing named as ... | [
"Contrastive Learning",
"Self Supervised Learning",
"One-Class SVM",
"Deep Learning"
] | https://openreview.net/pdf?id=XCUzATsVdU | https://openreview.net/forum?id=XCUzATsVdU | dmskMZMOvU | official_review | 1,728,531,685,099 | XCUzATsVdU | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission33/Reviewer_5y1S"
] | NLDL.org/2025/Conference | 2025 | title: A simple but effective strategy for contrastive learning
summary: The paper introduces a new method that mixes negatives selected with one-class SVM (OCSVM) for image-based contrastive learning. The method, called MiOC (Mixing OCSVM negatives), uses OCSVM to select inlier negative embeddings. These embeddings ar... |
XCUzATsVdU | One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning | [
"Dhruv Jain",
"Tsiry Mayet",
"Romain HÉRAULT",
"Romain MODZELEWSKI"
] | Recent studies on contrastive learning have emphasized carefully sampling and mixing negative samples.
This study introduces a novel and improved approach for generating synthetic negatives.
We propose a new method using One-Class Support Vector Machine (OCSVM) to guide in the selection process before mixing named as ... | [
"Contrastive Learning",
"Self Supervised Learning",
"One-Class SVM",
"Deep Learning"
] | https://openreview.net/pdf?id=XCUzATsVdU | https://openreview.net/forum?id=XCUzATsVdU | cUmd8fRYsK | official_review | 1,728,158,102,089 | XCUzATsVdU | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission33/Reviewer_5RzJ"
] | NLDL.org/2025/Conference | 2025 | title: Interesting method, but a few concerns on originality and importance
summary: The paper proposes to use one-class support-vector machine (OCSVM) to guide the selection process for generating synthetic negatives for contrastive learning. The authors base their analysis on the idea of MoCHI
of interpolation betwe... |
XCUzATsVdU | One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning | [
"Dhruv Jain",
"Tsiry Mayet",
"Romain HÉRAULT",
"Romain MODZELEWSKI"
] | Recent studies on contrastive learning have emphasized carefully sampling and mixing negative samples.
This study introduces a novel and improved approach for generating synthetic negatives.
We propose a new method using One-Class Support Vector Machine (OCSVM) to guide in the selection process before mixing named as ... | [
"Contrastive Learning",
"Self Supervised Learning",
"One-Class SVM",
"Deep Learning"
] | https://openreview.net/pdf?id=XCUzATsVdU | https://openreview.net/forum?id=XCUzATsVdU | b8MlXvlCYH | official_review | 1,727,366,425,467 | XCUzATsVdU | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission33/Reviewer_a8Mf"
] | NLDL.org/2025/Conference | 2025 | title: Review "One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning"
summary: The paper "One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning" discusses a novel approach to generate negative samples for contrastive learning by utilizing the one-class SVM. Contrastive learning dea... |
XCUzATsVdU | One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning | [
"Dhruv Jain",
"Tsiry Mayet",
"Romain HÉRAULT",
"Romain MODZELEWSKI"
] | Recent studies on contrastive learning have emphasized carefully sampling and mixing negative samples.
This study introduces a novel and improved approach for generating synthetic negatives.
We propose a new method using One-Class Support Vector Machine (OCSVM) to guide in the selection process before mixing named as ... | [
"Contrastive Learning",
"Self Supervised Learning",
"One-Class SVM",
"Deep Learning"
] | https://openreview.net/pdf?id=XCUzATsVdU | https://openreview.net/forum?id=XCUzATsVdU | I2VAGC75Ei | meta_review | 1,730,583,859,996 | XCUzATsVdU | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission33/Area_Chair_yuuB"
] | NLDL.org/2025/Conference | 2025 | metareview: This paper presents MiOC, a novel method that leverages One-Class SVM (OCSVM) to improve contrastive learning through the generation of challenging synthetic negative samples. The approach is promising, showing competitive performance against other methods on several benchmark datasets. However, the paper w... |
XCUzATsVdU | One-Class SVM-guided Negative Sampling for Enhanced Contrastive Learning | [
"Dhruv Jain",
"Tsiry Mayet",
"Romain HÉRAULT",
"Romain MODZELEWSKI"
] | Recent studies on contrastive learning have emphasized carefully sampling and mixing negative samples.
This study introduces a novel and improved approach for generating synthetic negatives.
We propose a new method using One-Class Support Vector Machine (OCSVM) to guide in the selection process before mixing named as ... | [
"Contrastive Learning",
"Self Supervised Learning",
"One-Class SVM",
"Deep Learning"
] | https://openreview.net/pdf?id=XCUzATsVdU | https://openreview.net/forum?id=XCUzATsVdU | CmvjKCnC01 | official_review | 1,728,309,742,331 | XCUzATsVdU | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission33/Reviewer_pWvU"
] | NLDL.org/2025/Conference | 2025 | title: Good read, sound approach, a few things could still be improved
summary: The authors present MiOC, an approach for generating synthetic negatives to improve classification performance. They describe the method and evaluate MiOC using various data sets. The results show that it performs better than existing appro... |
UQuETmoMQX | Towards concurrent real-time audio-aware agents with deep reinforcement learning | [
"Anton Debner",
"Vesa Hirvisalo"
] | Audio holds significant amount of information about our surroundings. It can be used to navigate, assess threats, communicate, as a source of curiosity, and to separate the sources of different sounds. Still, these rich properties of audio are not fully utilized by current video game agents.
We use spatial audio libr... | [
"audio",
"game engine",
"deep reinforcement learning",
"unity"
] | https://openreview.net/pdf?id=UQuETmoMQX | https://openreview.net/forum?id=UQuETmoMQX | xlxIJKbigK | official_review | 1,728,514,960,133 | UQuETmoMQX | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission32/Reviewer_ft3k"
] | NLDL.org/2025/Conference | 2025 | title: Review
summary: The paper describes the design of audio-based deep rl agents, in a multi-listener system.
The paper tackles a navigation problem: in a game engine, an agent tries to find the source of a noise. This is formalized as an RL problem, where the observations are visuals and sounds, the actions are si... |
UQuETmoMQX | Towards concurrent real-time audio-aware agents with deep reinforcement learning | [
"Anton Debner",
"Vesa Hirvisalo"
] | Audio holds significant amount of information about our surroundings. It can be used to navigate, assess threats, communicate, as a source of curiosity, and to separate the sources of different sounds. Still, these rich properties of audio are not fully utilized by current video game agents.
We use spatial audio libr... | [
"audio",
"game engine",
"deep reinforcement learning",
"unity"
] | https://openreview.net/pdf?id=UQuETmoMQX | https://openreview.net/forum?id=UQuETmoMQX | rWt94lGZXz | official_review | 1,727,879,252,339 | UQuETmoMQX | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission32/Reviewer_PkUU"
] | NLDL.org/2025/Conference | 2025 | title: Official Review
summary: This paper explores the use of deep reinforcement learning (DRL) to train audio-aware agents in video games, addressing the challenge of limited multi-listener support in game engines. The authors propose a distributed architecture where each client runs its own audio engine instance (Fi... |
UQuETmoMQX | Towards concurrent real-time audio-aware agents with deep reinforcement learning | [
"Anton Debner",
"Vesa Hirvisalo"
] | Audio holds significant amount of information about our surroundings. It can be used to navigate, assess threats, communicate, as a source of curiosity, and to separate the sources of different sounds. Still, these rich properties of audio are not fully utilized by current video game agents.
We use spatial audio libr... | [
"audio",
"game engine",
"deep reinforcement learning",
"unity"
] | https://openreview.net/pdf?id=UQuETmoMQX | https://openreview.net/forum?id=UQuETmoMQX | bE5W5KhdSP | official_review | 1,728,037,958,009 | UQuETmoMQX | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission32/Reviewer_ZpAp"
] | NLDL.org/2025/Conference | 2025 | title: Official Review of Submission32
summary: This paper explores the application of reinforcement learning to train audio-aware agents that utilize both visual and auditory cues in real-time environments, particularly in a hide-and-seek scenario. The paper aims to overcome the limitations of game engines that typica... |
UQuETmoMQX | Towards concurrent real-time audio-aware agents with deep reinforcement learning | [
"Anton Debner",
"Vesa Hirvisalo"
] | Audio holds significant amount of information about our surroundings. It can be used to navigate, assess threats, communicate, as a source of curiosity, and to separate the sources of different sounds. Still, these rich properties of audio are not fully utilized by current video game agents.
We use spatial audio libr... | [
"audio",
"game engine",
"deep reinforcement learning",
"unity"
] | https://openreview.net/pdf?id=UQuETmoMQX | https://openreview.net/forum?id=UQuETmoMQX | UODeHSDdLK | meta_review | 1,730,503,257,434 | UQuETmoMQX | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission32/Area_Chair_bUUd"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper addresses the (sometimes) overlooked area of audio based RL agents. The authors propose a solution where they focus on the parallelization of multi-listener systems, with a complete and well-motivated set of experiments. The work is both relevant and timely and can generate interesting discussions... |
UQuETmoMQX | Towards concurrent real-time audio-aware agents with deep reinforcement learning | [
"Anton Debner",
"Vesa Hirvisalo"
] | Audio holds significant amount of information about our surroundings. It can be used to navigate, assess threats, communicate, as a source of curiosity, and to separate the sources of different sounds. Still, these rich properties of audio are not fully utilized by current video game agents.
We use spatial audio libr... | [
"audio",
"game engine",
"deep reinforcement learning",
"unity"
] | https://openreview.net/pdf?id=UQuETmoMQX | https://openreview.net/forum?id=UQuETmoMQX | 5EeRlB4iW9 | official_review | 1,728,484,534,585 | UQuETmoMQX | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission32/Reviewer_6bjW"
] | NLDL.org/2025/Conference | 2025 | title: Paper showing why concurrent audio aware agents would be useful in a game engine setup and how DRL is useful in building such agents.
summary: The paper focuses on enabling deep reinforcement learning agents (PPO Based) to use audio cues for navigation. The authors demonstrate a method for incorporating multiple... |
UQuETmoMQX | Towards concurrent real-time audio-aware agents with deep reinforcement learning | [
"Anton Debner",
"Vesa Hirvisalo"
] | Audio holds significant amount of information about our surroundings. It can be used to navigate, assess threats, communicate, as a source of curiosity, and to separate the sources of different sounds. Still, these rich properties of audio are not fully utilized by current video game agents.
We use spatial audio libr... | [
"audio",
"game engine",
"deep reinforcement learning",
"unity"
] | https://openreview.net/pdf?id=UQuETmoMQX | https://openreview.net/forum?id=UQuETmoMQX | 1CIbkCaxrc | decision | 1,730,901,556,009 | UQuETmoMQX | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Oral)
comment: We recommend an oral and a poster presentation given the AC and reviewers recommendations. |
TP0ASAlrp2 | Deep Active Latent Surfaces for Medical Geometries | [
"Patrick Møller Jensen",
"Udaranga Wickramasinghe",
"Anders Dahl",
"Pascal Fua",
"Vedrana Andersen Dahl"
] | Shape priors have long been known to be effective when reconstructing 3D shapes from noisy or incomplete data. When using a deep-learning based shape representation, this often involves learning a latent representation, which can be either in the form of a single global vector or of multiple local ones. The latter all... | [
"Shape Models",
"Medical Image Processing",
"Autodecoders"
] | https://openreview.net/pdf?id=TP0ASAlrp2 | https://openreview.net/forum?id=TP0ASAlrp2 | vfZQbwpkem | official_review | 1,728,451,295,077 | TP0ASAlrp2 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission28/Reviewer_8zCr"
] | NLDL.org/2025/Conference | 2025 | title: Powerful and solid contributions to 3D reconstruction
summary: The 3D reconstruction problem is one of the important application cases in computer vision and medical bio-information processing. This paper makes new contributions to the field of 3D reconstruction, while also following the latest trends in the fie... |
TP0ASAlrp2 | Deep Active Latent Surfaces for Medical Geometries | [
"Patrick Møller Jensen",
"Udaranga Wickramasinghe",
"Anders Dahl",
"Pascal Fua",
"Vedrana Andersen Dahl"
] | Shape priors have long been known to be effective when reconstructing 3D shapes from noisy or incomplete data. When using a deep-learning based shape representation, this often involves learning a latent representation, which can be either in the form of a single global vector or of multiple local ones. The latter all... | [
"Shape Models",
"Medical Image Processing",
"Autodecoders"
] | https://openreview.net/pdf?id=TP0ASAlrp2 | https://openreview.net/forum?id=TP0ASAlrp2 | mZ34Wm80by | meta_review | 1,730,384,632,718 | TP0ASAlrp2 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission28/Area_Chair_tFPu"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper proposed a novel method for 3D shapes reconstruction by learning a mesh deformation. Reviewers found the proposed approach novel and conceptually interesting with solid experimental results and reproducibility. They also commend great paper presentation and high quality of the manuscript.
Overal... |
TP0ASAlrp2 | Deep Active Latent Surfaces for Medical Geometries | [
"Patrick Møller Jensen",
"Udaranga Wickramasinghe",
"Anders Dahl",
"Pascal Fua",
"Vedrana Andersen Dahl"
] | Shape priors have long been known to be effective when reconstructing 3D shapes from noisy or incomplete data. When using a deep-learning based shape representation, this often involves learning a latent representation, which can be either in the form of a single global vector or of multiple local ones. The latter all... | [
"Shape Models",
"Medical Image Processing",
"Autodecoders"
] | https://openreview.net/pdf?id=TP0ASAlrp2 | https://openreview.net/forum?id=TP0ASAlrp2 | izoy9iH0bs | official_review | 1,728,790,611,186 | TP0ASAlrp2 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission28/Reviewer_rExU"
] | NLDL.org/2025/Conference | 2025 | title: Interesting approach, method details are not clear enough, evaluated only on one dataset
summary: This paper introduces an active latent shape representation model for shape reconstruction by deforming a triangulated sphere to match the target shape. The proposed approach consists of two stages: training and fit... |
TP0ASAlrp2 | Deep Active Latent Surfaces for Medical Geometries | [
"Patrick Møller Jensen",
"Udaranga Wickramasinghe",
"Anders Dahl",
"Pascal Fua",
"Vedrana Andersen Dahl"
] | Shape priors have long been known to be effective when reconstructing 3D shapes from noisy or incomplete data. When using a deep-learning based shape representation, this often involves learning a latent representation, which can be either in the form of a single global vector or of multiple local ones. The latter all... | [
"Shape Models",
"Medical Image Processing",
"Autodecoders"
] | https://openreview.net/pdf?id=TP0ASAlrp2 | https://openreview.net/forum?id=TP0ASAlrp2 | fIpLOttTKu | official_review | 1,728,671,377,175 | TP0ASAlrp2 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission28/Reviewer_gdUj"
] | NLDL.org/2025/Conference | 2025 | title: Review
summary: This paper proposes DALS - a method for shape reconstruction by learning vertex displacements. The method is based two steps: first, learn an autoencoder for geometric shapes. Second, at inference time, optimize over the latent vectors at each vertex to generate an updated displacement of the ver... |
TP0ASAlrp2 | Deep Active Latent Surfaces for Medical Geometries | [
"Patrick Møller Jensen",
"Udaranga Wickramasinghe",
"Anders Dahl",
"Pascal Fua",
"Vedrana Andersen Dahl"
] | Shape priors have long been known to be effective when reconstructing 3D shapes from noisy or incomplete data. When using a deep-learning based shape representation, this often involves learning a latent representation, which can be either in the form of a single global vector or of multiple local ones. The latter all... | [
"Shape Models",
"Medical Image Processing",
"Autodecoders"
] | https://openreview.net/pdf?id=TP0ASAlrp2 | https://openreview.net/forum?id=TP0ASAlrp2 | 7NW3nMn3e6 | official_review | 1,726,920,771,961 | TP0ASAlrp2 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission28/Reviewer_byPa"
] | NLDL.org/2025/Conference | 2025 | title: Review "Deep Active Latent Surfaces for Medical Geometries"
summary: The paper proposes a novel auto-decoder method to represent 3D shapes by training a neural net that receives a latent vector and a point on a standard 3D sphere as input and predicts the offset from the 3D sphere vertex location to the actual v... |
TP0ASAlrp2 | Deep Active Latent Surfaces for Medical Geometries | [
"Patrick Møller Jensen",
"Udaranga Wickramasinghe",
"Anders Dahl",
"Pascal Fua",
"Vedrana Andersen Dahl"
] | Shape priors have long been known to be effective when reconstructing 3D shapes from noisy or incomplete data. When using a deep-learning based shape representation, this often involves learning a latent representation, which can be either in the form of a single global vector or of multiple local ones. The latter all... | [
"Shape Models",
"Medical Image Processing",
"Autodecoders"
] | https://openreview.net/pdf?id=TP0ASAlrp2 | https://openreview.net/forum?id=TP0ASAlrp2 | 3ebnkhClrN | decision | 1,730,901,555,748 | TP0ASAlrp2 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Oral)
comment: Given the AC positive recommendation, we recommend an oral and a poster presentation given the AC and reviewers recommendations. |
SPRdfOkuHw | Learning anomalies from graph: predicting compute node failures on HPC clusters | [
"Joze M. Rozanec",
"Roy Krumpak",
"Martin Molan",
"Andrea Bartolini"
] | Today, high-performance computing (HPC) systems play a crucial role in advancing artificial intelligence. Nevertheless, the estimated global data center electricity consumption in 2022 was around 1\% of the final global electricity demand. Therefore, as HPC systems advance towards Exascale computing, research is requir... | [
"Artificial Intelligence",
"Machine Learning",
"Graphs",
"HPC",
"Data Center",
"Anomalies Forecasting"
] | https://openreview.net/pdf?id=SPRdfOkuHw | https://openreview.net/forum?id=SPRdfOkuHw | sUWbFCyW8k | decision | 1,730,901,556,711 | SPRdfOkuHw | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Oral)
comment: We recommend an oral and a poster presentation given the AC and reviewers recommendations. |
SPRdfOkuHw | Learning anomalies from graph: predicting compute node failures on HPC clusters | [
"Joze M. Rozanec",
"Roy Krumpak",
"Martin Molan",
"Andrea Bartolini"
] | Today, high-performance computing (HPC) systems play a crucial role in advancing artificial intelligence. Nevertheless, the estimated global data center electricity consumption in 2022 was around 1\% of the final global electricity demand. Therefore, as HPC systems advance towards Exascale computing, research is requir... | [
"Artificial Intelligence",
"Machine Learning",
"Graphs",
"HPC",
"Data Center",
"Anomalies Forecasting"
] | https://openreview.net/pdf?id=SPRdfOkuHw | https://openreview.net/forum?id=SPRdfOkuHw | q5zUKgYh30 | meta_review | 1,730,366,298,167 | SPRdfOkuHw | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission51/Area_Chair_oAFF"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper presents a comparative analysis of various approaches for predicting issues in high-performance computing (HPC) systems. Strengths of the work include a detailed and insightful explanation of the dataset and a clear overview of the results. A key weakness, however, is the limited discussion on the... |
SPRdfOkuHw | Learning anomalies from graph: predicting compute node failures on HPC clusters | [
"Joze M. Rozanec",
"Roy Krumpak",
"Martin Molan",
"Andrea Bartolini"
] | Today, high-performance computing (HPC) systems play a crucial role in advancing artificial intelligence. Nevertheless, the estimated global data center electricity consumption in 2022 was around 1\% of the final global electricity demand. Therefore, as HPC systems advance towards Exascale computing, research is requir... | [
"Artificial Intelligence",
"Machine Learning",
"Graphs",
"HPC",
"Data Center",
"Anomalies Forecasting"
] | https://openreview.net/pdf?id=SPRdfOkuHw | https://openreview.net/forum?id=SPRdfOkuHw | jhvapMcRXX | official_review | 1,727,815,356,166 | SPRdfOkuHw | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission51/Reviewer_3Ake"
] | NLDL.org/2025/Conference | 2025 | title: Lack of novelty and mathematical justifications
summary: The paper compares different approaches in predicting issues in high-performance computing systems. For this, the authors utilize the M100 dataset consisting of telemetry data with known anomalies. In the experiments, the authors compare 4 different approa... |
SPRdfOkuHw | Learning anomalies from graph: predicting compute node failures on HPC clusters | [
"Joze M. Rozanec",
"Roy Krumpak",
"Martin Molan",
"Andrea Bartolini"
] | Today, high-performance computing (HPC) systems play a crucial role in advancing artificial intelligence. Nevertheless, the estimated global data center electricity consumption in 2022 was around 1\% of the final global electricity demand. Therefore, as HPC systems advance towards Exascale computing, research is requir... | [
"Artificial Intelligence",
"Machine Learning",
"Graphs",
"HPC",
"Data Center",
"Anomalies Forecasting"
] | https://openreview.net/pdf?id=SPRdfOkuHw | https://openreview.net/forum?id=SPRdfOkuHw | ZiJaBkIpJS | official_review | 1,727,463,506,733 | SPRdfOkuHw | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission51/Reviewer_PR3L"
] | NLDL.org/2025/Conference | 2025 | title: Review of the "Learning anomalies from graph" paper
summary: This paper addresses the pressing challenge of predicting compute node downtimes in HPC systems, highlighting the importance of predictive maintenance for enhancing system sustainability and efficiency. The authors employ a data-driven approach using p... |
SPRdfOkuHw | Learning anomalies from graph: predicting compute node failures on HPC clusters | [
"Joze M. Rozanec",
"Roy Krumpak",
"Martin Molan",
"Andrea Bartolini"
] | Today, high-performance computing (HPC) systems play a crucial role in advancing artificial intelligence. Nevertheless, the estimated global data center electricity consumption in 2022 was around 1\% of the final global electricity demand. Therefore, as HPC systems advance towards Exascale computing, research is requir... | [
"Artificial Intelligence",
"Machine Learning",
"Graphs",
"HPC",
"Data Center",
"Anomalies Forecasting"
] | https://openreview.net/pdf?id=SPRdfOkuHw | https://openreview.net/forum?id=SPRdfOkuHw | IYaBhALfeT | official_review | 1,727,161,730,372 | SPRdfOkuHw | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission51/Reviewer_AqLd"
] | NLDL.org/2025/Conference | 2025 | title: Comment
summary: The paper is about graph anomaly detection, which is an interesting topic. In this paper, authors aim to predict compute node failures on HPC clusters. The paper is well written and well organized. However, there are several concerns in the current version of the paper that addressing them will ... |
SPRdfOkuHw | Learning anomalies from graph: predicting compute node failures on HPC clusters | [
"Joze M. Rozanec",
"Roy Krumpak",
"Martin Molan",
"Andrea Bartolini"
] | Today, high-performance computing (HPC) systems play a crucial role in advancing artificial intelligence. Nevertheless, the estimated global data center electricity consumption in 2022 was around 1\% of the final global electricity demand. Therefore, as HPC systems advance towards Exascale computing, research is requir... | [
"Artificial Intelligence",
"Machine Learning",
"Graphs",
"HPC",
"Data Center",
"Anomalies Forecasting"
] | https://openreview.net/pdf?id=SPRdfOkuHw | https://openreview.net/forum?id=SPRdfOkuHw | 7ITj0Lz5zn | official_review | 1,728,321,620,373 | SPRdfOkuHw | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission51/Reviewer_jTLt"
] | NLDL.org/2025/Conference | 2025 | title: The study focuses on predicting compute node failures in HPC clusters with learning form graph approach.
summary: The study compares four machine learning approaches for predicting compute node downtime, three of which are based on graph embeddings, and compares the results.
strengths: Feature extraction methods... |
SBbh4PvJrC | Similarity-Based Intent Detection Using an Enhanced Siamese Network | [] | In Natural Language Understanding (NLU), intent detection is crucial for improving human-computer interaction. However, traditional supervised learning models rely heavily on large annotated datasets, limiting their effectiveness in low-resource scenarios with limited labeled data. Siamese networks, which are effective... | [
"Intent detection",
"Siamese network",
"Dialogue system",
"Similarity metrics"
] | https://openreview.net/pdf?id=SBbh4PvJrC | https://openreview.net/forum?id=SBbh4PvJrC | zbdrr4DM6d | official_review | 1,727,280,810,148 | SBbh4PvJrC | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission12/Reviewer_6Brq"
] | NLDL.org/2025/Conference | 2025 | title: The claim in the conclusion is not well-founded
summary: This work proposes a modification to the contrastive loss, where an extra dense layer is added after the distance calculation. This allows for using several different distance measures and combine the result though the dense layer to a single "similarity s... |
SBbh4PvJrC | Similarity-Based Intent Detection Using an Enhanced Siamese Network | [] | In Natural Language Understanding (NLU), intent detection is crucial for improving human-computer interaction. However, traditional supervised learning models rely heavily on large annotated datasets, limiting their effectiveness in low-resource scenarios with limited labeled data. Siamese networks, which are effective... | [
"Intent detection",
"Siamese network",
"Dialogue system",
"Similarity metrics"
] | https://openreview.net/pdf?id=SBbh4PvJrC | https://openreview.net/forum?id=SBbh4PvJrC | vP0nfwv4w1 | official_review | 1,726,854,673,523 | SBbh4PvJrC | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission12/Reviewer_Mdjj"
] | NLDL.org/2025/Conference | 2025 | title: Review for "Similarity-Based Intent Detection Using an Enhanced Siamese Network"
summary: The paper proposes a novel variant of Siamese networks for intent detection in which multiple notions of distance (esp. Manhattan, Euclidean, Cosine) are merged to achieve improved results. Experiments on two data sets reve... |
SBbh4PvJrC | Similarity-Based Intent Detection Using an Enhanced Siamese Network | [] | In Natural Language Understanding (NLU), intent detection is crucial for improving human-computer interaction. However, traditional supervised learning models rely heavily on large annotated datasets, limiting their effectiveness in low-resource scenarios with limited labeled data. Siamese networks, which are effective... | [
"Intent detection",
"Siamese network",
"Dialogue system",
"Similarity metrics"
] | https://openreview.net/pdf?id=SBbh4PvJrC | https://openreview.net/forum?id=SBbh4PvJrC | P12X60N9SI | decision | 1,730,901,554,687 | SBbh4PvJrC | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Reject |
SBbh4PvJrC | Similarity-Based Intent Detection Using an Enhanced Siamese Network | [] | In Natural Language Understanding (NLU), intent detection is crucial for improving human-computer interaction. However, traditional supervised learning models rely heavily on large annotated datasets, limiting their effectiveness in low-resource scenarios with limited labeled data. Siamese networks, which are effective... | [
"Intent detection",
"Siamese network",
"Dialogue system",
"Similarity metrics"
] | https://openreview.net/pdf?id=SBbh4PvJrC | https://openreview.net/forum?id=SBbh4PvJrC | JZCcxrgkfl | meta_review | 1,730,723,324,354 | SBbh4PvJrC | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission12/Area_Chair_hCjo"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper introduces a similarity based Siamese network for intent detection. Several similarities/dissimilarities between latent representations of input text and an intent are computed and combined into one representation, which is further processed for classification. The model outperforms the SOTA on tw... |
SBbh4PvJrC | Similarity-Based Intent Detection Using an Enhanced Siamese Network | [] | In Natural Language Understanding (NLU), intent detection is crucial for improving human-computer interaction. However, traditional supervised learning models rely heavily on large annotated datasets, limiting their effectiveness in low-resource scenarios with limited labeled data. Siamese networks, which are effective... | [
"Intent detection",
"Siamese network",
"Dialogue system",
"Similarity metrics"
] | https://openreview.net/pdf?id=SBbh4PvJrC | https://openreview.net/forum?id=SBbh4PvJrC | FF5Rme3FrJ | official_review | 1,727,861,169,417 | SBbh4PvJrC | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission12/Reviewer_zcwh"
] | NLDL.org/2025/Conference | 2025 | title: Saturating Intent-detection benchmarks
summary: The paper proposes siamese bi-lstm networks combined with both euclidean and cosine similarity to solve intent detection. The suggested model effectively saturates both the ATIS and SNIPS datasets, setting a new SOTA.
strengths: The model performs very well on the ... |
SBbh4PvJrC | Similarity-Based Intent Detection Using an Enhanced Siamese Network | [] | In Natural Language Understanding (NLU), intent detection is crucial for improving human-computer interaction. However, traditional supervised learning models rely heavily on large annotated datasets, limiting their effectiveness in low-resource scenarios with limited labeled data. Siamese networks, which are effective... | [
"Intent detection",
"Siamese network",
"Dialogue system",
"Similarity metrics"
] | https://openreview.net/pdf?id=SBbh4PvJrC | https://openreview.net/forum?id=SBbh4PvJrC | 7r5KfP7O1K | official_review | 1,726,667,561,731 | SBbh4PvJrC | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission12/Reviewer_8o6U"
] | NLDL.org/2025/Conference | 2025 | title: Unverified claims, poor methodology, and absent analysis for overused dataset of Intent Detection
summary: This work introduces a study on intent classification through the lens of siamese networks. It uses 2 datasets for intent (ATIS and SNIPS) , trains a BiLSTM in a siamese setting and tests a variety of simil... |
S5wu3nVT1a | Keeping it Simple – Computational Resources in Deep Generative versus Traditional Methods for Synthetic Tabular Data Generation in Healthcare | [] | Synthetic data has emerged as a solution to address data access challenges in healthcare, particularly for accelerating AI tool development. Deep generative methods, including generative adversarial networks, variational autoencoders, and diffusion models, have gained prominence for creating realistic and representativ... | [
"synthetic data",
"healthcare data",
"deep generative models",
"traditional statistical generative methods",
"computational resources",
"benchmarking"
] | https://openreview.net/pdf?id=S5wu3nVT1a | https://openreview.net/forum?id=S5wu3nVT1a | XSbGAXZMxl | meta_review | 1,730,472,611,782 | S5wu3nVT1a | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission21/Area_Chair_oV3g"
] | NLDL.org/2025/Conference | 2025 | metareview: This paper addresses an important yet well-known concern regarding the computational resource demands of deep generative models versus traditional statistical approaches for synthetic healthcare data. While the study highlights the efficiency of Gaussian copula models in comparison to deep generative models... |
S5wu3nVT1a | Keeping it Simple – Computational Resources in Deep Generative versus Traditional Methods for Synthetic Tabular Data Generation in Healthcare | [] | Synthetic data has emerged as a solution to address data access challenges in healthcare, particularly for accelerating AI tool development. Deep generative methods, including generative adversarial networks, variational autoencoders, and diffusion models, have gained prominence for creating realistic and representativ... | [
"synthetic data",
"healthcare data",
"deep generative models",
"traditional statistical generative methods",
"computational resources",
"benchmarking"
] | https://openreview.net/pdf?id=S5wu3nVT1a | https://openreview.net/forum?id=S5wu3nVT1a | IhmztxUbjF | official_review | 1,728,892,976,441 | S5wu3nVT1a | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission21/Reviewer_rJLi"
] | NLDL.org/2025/Conference | 2025 | title: The paper lacks novelty and suffers from weak experimental design, insufficient analysis, and limited practical relevance.
summary: The paper explores the computational resource requirements and performance of deep generative models (such as CTGAN and TVAE) compared to traditional methods for generating syntheti... |
S5wu3nVT1a | Keeping it Simple – Computational Resources in Deep Generative versus Traditional Methods for Synthetic Tabular Data Generation in Healthcare | [] | Synthetic data has emerged as a solution to address data access challenges in healthcare, particularly for accelerating AI tool development. Deep generative methods, including generative adversarial networks, variational autoencoders, and diffusion models, have gained prominence for creating realistic and representativ... | [
"synthetic data",
"healthcare data",
"deep generative models",
"traditional statistical generative methods",
"computational resources",
"benchmarking"
] | https://openreview.net/pdf?id=S5wu3nVT1a | https://openreview.net/forum?id=S5wu3nVT1a | GS8rDr8lBr | official_review | 1,727,099,563,916 | S5wu3nVT1a | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission21/Reviewer_6F7G"
] | NLDL.org/2025/Conference | 2025 | title: Nice idea, limited execution, muddled presentation
summary: This paper performs an experiment on synthetic healthcare data, comparing a simple statistical model (Gaussian copula) with two more sophisticated machine learning methods (a generative adversarial network and a variational autoencoder, both designed fo... |
S5wu3nVT1a | Keeping it Simple – Computational Resources in Deep Generative versus Traditional Methods for Synthetic Tabular Data Generation in Healthcare | [] | Synthetic data has emerged as a solution to address data access challenges in healthcare, particularly for accelerating AI tool development. Deep generative methods, including generative adversarial networks, variational autoencoders, and diffusion models, have gained prominence for creating realistic and representativ... | [
"synthetic data",
"healthcare data",
"deep generative models",
"traditional statistical generative methods",
"computational resources",
"benchmarking"
] | https://openreview.net/pdf?id=S5wu3nVT1a | https://openreview.net/forum?id=S5wu3nVT1a | GP40araFN3 | official_review | 1,728,956,154,402 | S5wu3nVT1a | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission21/Reviewer_42fL"
] | NLDL.org/2025/Conference | 2025 | title: Review of Keeping it Simple – Computational Resources in Deep Gener- ative versus Traditional Methods for Synthetic Tabular Data Generation in Healthcare
summary: The purpose of the study is aimed at supporting the broader healthcare community and challenges faced with safe data access with the fast paced growth... |
S5wu3nVT1a | Keeping it Simple – Computational Resources in Deep Generative versus Traditional Methods for Synthetic Tabular Data Generation in Healthcare | [] | Synthetic data has emerged as a solution to address data access challenges in healthcare, particularly for accelerating AI tool development. Deep generative methods, including generative adversarial networks, variational autoencoders, and diffusion models, have gained prominence for creating realistic and representativ... | [
"synthetic data",
"healthcare data",
"deep generative models",
"traditional statistical generative methods",
"computational resources",
"benchmarking"
] | https://openreview.net/pdf?id=S5wu3nVT1a | https://openreview.net/forum?id=S5wu3nVT1a | CNwgcZPGdL | decision | 1,730,901,555,377 | S5wu3nVT1a | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Reject |
S5wu3nVT1a | Keeping it Simple – Computational Resources in Deep Generative versus Traditional Methods for Synthetic Tabular Data Generation in Healthcare | [] | Synthetic data has emerged as a solution to address data access challenges in healthcare, particularly for accelerating AI tool development. Deep generative methods, including generative adversarial networks, variational autoencoders, and diffusion models, have gained prominence for creating realistic and representativ... | [
"synthetic data",
"healthcare data",
"deep generative models",
"traditional statistical generative methods",
"computational resources",
"benchmarking"
] | https://openreview.net/pdf?id=S5wu3nVT1a | https://openreview.net/forum?id=S5wu3nVT1a | 2ydz6z7wlM | official_review | 1,728,247,358,034 | S5wu3nVT1a | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission21/Reviewer_2fBg"
] | NLDL.org/2025/Conference | 2025 | title: The initial review
summary: This paper presents a comparative study that evaluates computational resource consumption and data quality in generating synthetic tabular data. The authors test the traditional Gaussian Copula method and popular deep generative methods, such as TVAE and CTGAN, using the Dutch cancer ... |
RqdaGXhoTa | Machine Learning-Based Coastal Terrain Classification in Tropical Regions Using Multispectral UAV Imaging: A Comparative Study of Random Forest and SVM Models | [] | Advances in various technologies and machine learning (ML) are transforming the field of remote sensing. This study proposes an ML-centered methodology for classifying coastal terrain in tropical coastal regions using multispectral unmanned aerial vehicle (UAV) image inputs. The objective is to identify suitable ML alg... | [
"UAV",
"Multispectral Imaging",
"SVM",
"Random Forest"
] | https://openreview.net/pdf?id=RqdaGXhoTa | https://openreview.net/forum?id=RqdaGXhoTa | l7k6z5GMiw | meta_review | 1,730,420,515,635 | RqdaGXhoTa | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission34/Area_Chair_aSEx"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper discusses an interesting topic of using machine learning to identify coastal terrain acquired by UAV multispectral sensors. Reviewers rise a number of questions, with a general negative trend regarding the methodology novelty (application of existing and old methods to a new dataset), the validati... |
RqdaGXhoTa | Machine Learning-Based Coastal Terrain Classification in Tropical Regions Using Multispectral UAV Imaging: A Comparative Study of Random Forest and SVM Models | [] | Advances in various technologies and machine learning (ML) are transforming the field of remote sensing. This study proposes an ML-centered methodology for classifying coastal terrain in tropical coastal regions using multispectral unmanned aerial vehicle (UAV) image inputs. The objective is to identify suitable ML alg... | [
"UAV",
"Multispectral Imaging",
"SVM",
"Random Forest"
] | https://openreview.net/pdf?id=RqdaGXhoTa | https://openreview.net/forum?id=RqdaGXhoTa | cN5QHJsDYC | official_review | 1,728,514,749,365 | RqdaGXhoTa | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission34/Reviewer_mNAm"
] | NLDL.org/2025/Conference | 2025 | title: Comparison of SVM and Random Forest for Coastal Terrain Classification: Lacking Novelty and Comparison with State-of-the-Art Techniques
summary: This paper presents a comparison between Support Vector Machines (SVM) and Random Forest (RF) for coastal terrain classification (segmentation). The authors used a data... |
RqdaGXhoTa | Machine Learning-Based Coastal Terrain Classification in Tropical Regions Using Multispectral UAV Imaging: A Comparative Study of Random Forest and SVM Models | [] | Advances in various technologies and machine learning (ML) are transforming the field of remote sensing. This study proposes an ML-centered methodology for classifying coastal terrain in tropical coastal regions using multispectral unmanned aerial vehicle (UAV) image inputs. The objective is to identify suitable ML alg... | [
"UAV",
"Multispectral Imaging",
"SVM",
"Random Forest"
] | https://openreview.net/pdf?id=RqdaGXhoTa | https://openreview.net/forum?id=RqdaGXhoTa | aKRNkn87hD | official_review | 1,728,478,686,852 | RqdaGXhoTa | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission34/Reviewer_Wjcx"
] | NLDL.org/2025/Conference | 2025 | title: The motivation behind the paper is overall clearly presented and contextualized. The goal of the work is also carefully and clearly described. The architecture and the metrics selected are reliable. Big limits on degree of novelty, data collection and validation.
summary: The motivation behind the paper is clea... |
RqdaGXhoTa | Machine Learning-Based Coastal Terrain Classification in Tropical Regions Using Multispectral UAV Imaging: A Comparative Study of Random Forest and SVM Models | [] | Advances in various technologies and machine learning (ML) are transforming the field of remote sensing. This study proposes an ML-centered methodology for classifying coastal terrain in tropical coastal regions using multispectral unmanned aerial vehicle (UAV) image inputs. The objective is to identify suitable ML alg... | [
"UAV",
"Multispectral Imaging",
"SVM",
"Random Forest"
] | https://openreview.net/pdf?id=RqdaGXhoTa | https://openreview.net/forum?id=RqdaGXhoTa | SZAgH6xeTU | official_review | 1,727,009,316,629 | RqdaGXhoTa | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission34/Reviewer_v4h7"
] | NLDL.org/2025/Conference | 2025 | title: Comparison of RF and SVM on k-means clustered Vegetation Indices
summary: In brief the paper describes a pipeline:
- UAV images are processed using onboard capabilities, to genereate several different VI (vegetation indices)
- Labels are generated using k-means, with a k between 2 and 8. (further in the paper it... |
RqdaGXhoTa | Machine Learning-Based Coastal Terrain Classification in Tropical Regions Using Multispectral UAV Imaging: A Comparative Study of Random Forest and SVM Models | [] | Advances in various technologies and machine learning (ML) are transforming the field of remote sensing. This study proposes an ML-centered methodology for classifying coastal terrain in tropical coastal regions using multispectral unmanned aerial vehicle (UAV) image inputs. The objective is to identify suitable ML alg... | [
"UAV",
"Multispectral Imaging",
"SVM",
"Random Forest"
] | https://openreview.net/pdf?id=RqdaGXhoTa | https://openreview.net/forum?id=RqdaGXhoTa | Gt1lNA7FTl | official_review | 1,726,526,543,516 | RqdaGXhoTa | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission34/Reviewer_cYsE"
] | NLDL.org/2025/Conference | 2025 | title: Dataset represents trivial task, not deep learning
summary: The paper first introduces a new dataset consisting of multi-spectral UAV images, captured in a coastal area of the Philippines. The purpose of this dataset is to investigate different methods for pixel-wise classification of these images into one of se... |
RqdaGXhoTa | Machine Learning-Based Coastal Terrain Classification in Tropical Regions Using Multispectral UAV Imaging: A Comparative Study of Random Forest and SVM Models | [] | Advances in various technologies and machine learning (ML) are transforming the field of remote sensing. This study proposes an ML-centered methodology for classifying coastal terrain in tropical coastal regions using multispectral unmanned aerial vehicle (UAV) image inputs. The objective is to identify suitable ML alg... | [
"UAV",
"Multispectral Imaging",
"SVM",
"Random Forest"
] | https://openreview.net/pdf?id=RqdaGXhoTa | https://openreview.net/forum?id=RqdaGXhoTa | 6wbCQ2Z3zU | decision | 1,730,901,555,978 | RqdaGXhoTa | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Reject |
QswzbrMy3R | Interpretable Function Approximation with Gaussian Processes in Value-Based Model-Free Reinforcement Learning | [
"Matthijs van der Lende",
"Matthia Sabatelli",
"Juan Cardenas-Cartagena"
] | Estimating value functions in Reinforcement Learning (RL) for continuous spaces is challenging. While traditional function approximators, such as linear models, offer interpretability, they are limited in their complexity. In contrast, deep neural networks can model more complex functions but are less interpretable. Ga... | [
"reinforcement learning",
"gaussian process",
"deep learning",
"uncertainty estimation"
] | https://openreview.net/pdf?id=QswzbrMy3R | https://openreview.net/forum?id=QswzbrMy3R | xQmF0g3QLA | official_review | 1,728,511,249,006 | QswzbrMy3R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission9/Reviewer_WsxQ"
] | NLDL.org/2025/Conference | 2025 | title: A well-writen paper on the use of Gaussian processes as function approximators in RL with no significant shortcomings
summary: In this paper, the authors introduce a novel framework based on Gaussian processes (GP) used for off-policy and on-policy learning. The main motivation is the fact that GPS offers uncert... |
QswzbrMy3R | Interpretable Function Approximation with Gaussian Processes in Value-Based Model-Free Reinforcement Learning | [
"Matthijs van der Lende",
"Matthia Sabatelli",
"Juan Cardenas-Cartagena"
] | Estimating value functions in Reinforcement Learning (RL) for continuous spaces is challenging. While traditional function approximators, such as linear models, offer interpretability, they are limited in their complexity. In contrast, deep neural networks can model more complex functions but are less interpretable. Ga... | [
"reinforcement learning",
"gaussian process",
"deep learning",
"uncertainty estimation"
] | https://openreview.net/pdf?id=QswzbrMy3R | https://openreview.net/forum?id=QswzbrMy3R | WjTDxrqUGT | meta_review | 1,730,371,555,412 | QswzbrMy3R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission9/Area_Chair_Ae89"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper presents a study that explores the use of GPs for Q-function approximation, which trades-off interpretability vs. performance. While the reviewers raised concerns about the novelty and significance of the outcomes, the analysis is sound, and the insights are useful for the RL community. Hence, wit... |
QswzbrMy3R | Interpretable Function Approximation with Gaussian Processes in Value-Based Model-Free Reinforcement Learning | [
"Matthijs van der Lende",
"Matthia Sabatelli",
"Juan Cardenas-Cartagena"
] | Estimating value functions in Reinforcement Learning (RL) for continuous spaces is challenging. While traditional function approximators, such as linear models, offer interpretability, they are limited in their complexity. In contrast, deep neural networks can model more complex functions but are less interpretable. Ga... | [
"reinforcement learning",
"gaussian process",
"deep learning",
"uncertainty estimation"
] | https://openreview.net/pdf?id=QswzbrMy3R | https://openreview.net/forum?id=QswzbrMy3R | TEoG3LKlbJ | official_review | 1,727,982,251,966 | QswzbrMy3R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission9/Reviewer_PyJD"
] | NLDL.org/2025/Conference | 2025 | title: Contribution is weak and writing should be formal
summary: Gaussian processes are a non-standard function approximator for reinforcement learning value functions. RL value functions are usually approximated by deep neural networks or kernel regression. The GP is a flexible non-parametric estimator and has been s... |
QswzbrMy3R | Interpretable Function Approximation with Gaussian Processes in Value-Based Model-Free Reinforcement Learning | [
"Matthijs van der Lende",
"Matthia Sabatelli",
"Juan Cardenas-Cartagena"
] | Estimating value functions in Reinforcement Learning (RL) for continuous spaces is challenging. While traditional function approximators, such as linear models, offer interpretability, they are limited in their complexity. In contrast, deep neural networks can model more complex functions but are less interpretable. Ga... | [
"reinforcement learning",
"gaussian process",
"deep learning",
"uncertainty estimation"
] | https://openreview.net/pdf?id=QswzbrMy3R | https://openreview.net/forum?id=QswzbrMy3R | MqXSiuu2OR | decision | 1,730,901,554,501 | QswzbrMy3R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Poster)
comment: We recommend a poster presentation given the AC and reviewers recommendations. |
QswzbrMy3R | Interpretable Function Approximation with Gaussian Processes in Value-Based Model-Free Reinforcement Learning | [
"Matthijs van der Lende",
"Matthia Sabatelli",
"Juan Cardenas-Cartagena"
] | Estimating value functions in Reinforcement Learning (RL) for continuous spaces is challenging. While traditional function approximators, such as linear models, offer interpretability, they are limited in their complexity. In contrast, deep neural networks can model more complex functions but are less interpretable. Ga... | [
"reinforcement learning",
"gaussian process",
"deep learning",
"uncertainty estimation"
] | https://openreview.net/pdf?id=QswzbrMy3R | https://openreview.net/forum?id=QswzbrMy3R | 1H1eD4mWx2 | official_review | 1,728,628,889,709 | QswzbrMy3R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission9/Reviewer_GeCz"
] | NLDL.org/2025/Conference | 2025 | title: Review
summary: This paper presents a method for using Gaussian processes for approximate Q-learning. It extends GP-Q and GP-SARSA with SVGP and DGP for state-action value approximation. The experiments, though limited to two fairly simple discrete control tasks (CartPole and Lunar Lander), show that the propose... |
Q2wVVeOpz8 | Zero-Shot Open-Vocabulary OOD Object Detection and Grounding using Vision Language Models | [
"Poulami Sinhamahapatra",
"Shirsha Bose",
"Karsten Roscher",
"Stephan Günnemann"
] | Automated driving involves complex perception tasks that require a precise understanding of diverse traffic scenarios and confident navigation. Traditional data-driven algorithms trained on closed-set data often fail to generalize upon out-of-distribution (OOD) and edge cases. Recently, Large Vision Language Models (LV... | [
"OOD object detection",
"zero-shot",
"open-vocabulary",
"segmentation",
"autonomous driving",
"vision language models"
] | https://openreview.net/pdf?id=Q2wVVeOpz8 | https://openreview.net/forum?id=Q2wVVeOpz8 | yR3PNyixbv | official_review | 1,727,613,603,565 | Q2wVVeOpz8 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission49/Reviewer_irU7"
] | NLDL.org/2025/Conference | 2025 | title: Official Review
summary: The work proposes a zero-shot framework for OOD object detection and grounding in an autonomous vehicle setting. The framework leverages the capabilities of frozen large vision language models (LVLM) to first detect all objects in the image. In-domain objects are then detected via prompt... |
Q2wVVeOpz8 | Zero-Shot Open-Vocabulary OOD Object Detection and Grounding using Vision Language Models | [
"Poulami Sinhamahapatra",
"Shirsha Bose",
"Karsten Roscher",
"Stephan Günnemann"
] | Automated driving involves complex perception tasks that require a precise understanding of diverse traffic scenarios and confident navigation. Traditional data-driven algorithms trained on closed-set data often fail to generalize upon out-of-distribution (OOD) and edge cases. Recently, Large Vision Language Models (LV... | [
"OOD object detection",
"zero-shot",
"open-vocabulary",
"segmentation",
"autonomous driving",
"vision language models"
] | https://openreview.net/pdf?id=Q2wVVeOpz8 | https://openreview.net/forum?id=Q2wVVeOpz8 | X5cl5s4HEx | official_review | 1,727,005,085,060 | Q2wVVeOpz8 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission49/Reviewer_XbXC"
] | NLDL.org/2025/Conference | 2025 | title: Good paper which presents a zero-shot framework for OOD object detection
summary: SUMMARY: This paper presents two zero-shot algorithms for out-of-distribution (OOD) object detection using Large Vision Language Models (LVLMs). Experiment shows that the proposed method outperforms its competitors.
strengths: 1. T... |
Q2wVVeOpz8 | Zero-Shot Open-Vocabulary OOD Object Detection and Grounding using Vision Language Models | [
"Poulami Sinhamahapatra",
"Shirsha Bose",
"Karsten Roscher",
"Stephan Günnemann"
] | Automated driving involves complex perception tasks that require a precise understanding of diverse traffic scenarios and confident navigation. Traditional data-driven algorithms trained on closed-set data often fail to generalize upon out-of-distribution (OOD) and edge cases. Recently, Large Vision Language Models (LV... | [
"OOD object detection",
"zero-shot",
"open-vocabulary",
"segmentation",
"autonomous driving",
"vision language models"
] | https://openreview.net/pdf?id=Q2wVVeOpz8 | https://openreview.net/forum?id=Q2wVVeOpz8 | RDzLXOPtxl | meta_review | 1,730,469,016,586 | Q2wVVeOpz8 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission49/Area_Chair_733k"
] | NLDL.org/2025/Conference | 2025 | metareview: This paper suggests to rely on large vision-language model (LVLM) to perform zero-shot out-of-domain (OOD) object detection.
All reviewers agreed about the importance of the problem, the relevance of the techniques involved, the clarity of the paper, and the significance (to some extent) of the experimenta... |
Q2wVVeOpz8 | Zero-Shot Open-Vocabulary OOD Object Detection and Grounding using Vision Language Models | [
"Poulami Sinhamahapatra",
"Shirsha Bose",
"Karsten Roscher",
"Stephan Günnemann"
] | Automated driving involves complex perception tasks that require a precise understanding of diverse traffic scenarios and confident navigation. Traditional data-driven algorithms trained on closed-set data often fail to generalize upon out-of-distribution (OOD) and edge cases. Recently, Large Vision Language Models (LV... | [
"OOD object detection",
"zero-shot",
"open-vocabulary",
"segmentation",
"autonomous driving",
"vision language models"
] | https://openreview.net/pdf?id=Q2wVVeOpz8 | https://openreview.net/forum?id=Q2wVVeOpz8 | 7YrRo7WfRI | official_review | 1,727,938,970,020 | Q2wVVeOpz8 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission49/Reviewer_a9Xt"
] | NLDL.org/2025/Conference | 2025 | title: Review for submission #49
summary: This work proposes to leverage the Large vision-language model (LVLM) to perform zero-shot out-of-domain (OOD) object detection. The authors first query all the foreground instances within the scenes and propose two algorithms to determine the plausible OOD instances. They also... |
Q2wVVeOpz8 | Zero-Shot Open-Vocabulary OOD Object Detection and Grounding using Vision Language Models | [
"Poulami Sinhamahapatra",
"Shirsha Bose",
"Karsten Roscher",
"Stephan Günnemann"
] | Automated driving involves complex perception tasks that require a precise understanding of diverse traffic scenarios and confident navigation. Traditional data-driven algorithms trained on closed-set data often fail to generalize upon out-of-distribution (OOD) and edge cases. Recently, Large Vision Language Models (LV... | [
"OOD object detection",
"zero-shot",
"open-vocabulary",
"segmentation",
"autonomous driving",
"vision language models"
] | https://openreview.net/pdf?id=Q2wVVeOpz8 | https://openreview.net/forum?id=Q2wVVeOpz8 | 2BHAI5vlFo | decision | 1,730,901,556,596 | Q2wVVeOpz8 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Oral)
comment: Given the AC positive recommendation, we recommend an oral and a poster presentation given the AC and reviewers recommendations. |
Pyqnc9eWhB | Graph Counterfactual Explainable AI via Latent Space Traversal | [
"Andreas Abildtrup Hansen",
"Paraskevas Pegios",
"Anna Calissano",
"Aasa Feragen"
] | Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. \textit{Counterfactual explanations} aim to explain predictions by finding the ``nearest'' in-distribution alternative input whose pre... | [
"Explainable AI",
"Counterfactual explanations",
"graph",
"equivariance",
"invariance",
"symmetry",
"VAE"
] | https://openreview.net/pdf?id=Pyqnc9eWhB | https://openreview.net/forum?id=Pyqnc9eWhB | xF1Ae8ww1g | decision | 1,730,901,556,586 | Pyqnc9eWhB | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Oral)
comment: Given the AC positive recommendation, we recommend an oral and a poster presentation given the AC and reviewers recommendations. |
Pyqnc9eWhB | Graph Counterfactual Explainable AI via Latent Space Traversal | [
"Andreas Abildtrup Hansen",
"Paraskevas Pegios",
"Anna Calissano",
"Aasa Feragen"
] | Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. \textit{Counterfactual explanations} aim to explain predictions by finding the ``nearest'' in-distribution alternative input whose pre... | [
"Explainable AI",
"Counterfactual explanations",
"graph",
"equivariance",
"invariance",
"symmetry",
"VAE"
] | https://openreview.net/pdf?id=Pyqnc9eWhB | https://openreview.net/forum?id=Pyqnc9eWhB | qAjeonrhDM | official_review | 1,728,308,544,226 | Pyqnc9eWhB | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission48/Reviewer_DH66"
] | NLDL.org/2025/Conference | 2025 | title: This paper presents several intriguing ideas but my current assessment is just below the acceptance threshold.
summary: This paper presents several intriguing ideas. Firstly, it introduces a generative modeling method called PEGVAE, which utilizes VAE for graph instances. Unlike image instances, graph instances ... |
Pyqnc9eWhB | Graph Counterfactual Explainable AI via Latent Space Traversal | [
"Andreas Abildtrup Hansen",
"Paraskevas Pegios",
"Anna Calissano",
"Aasa Feragen"
] | Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. \textit{Counterfactual explanations} aim to explain predictions by finding the ``nearest'' in-distribution alternative input whose pre... | [
"Explainable AI",
"Counterfactual explanations",
"graph",
"equivariance",
"invariance",
"symmetry",
"VAE"
] | https://openreview.net/pdf?id=Pyqnc9eWhB | https://openreview.net/forum?id=Pyqnc9eWhB | XsLVvAxx1Y | official_review | 1,728,313,258,548 | Pyqnc9eWhB | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission48/Reviewer_mmEe"
] | NLDL.org/2025/Conference | 2025 | title: Clear approach to counterfactual explanations for graph classifiers
summary: This paper presents a method that produces counterfactual explanations for graph classifiers. At the core of the method, there is a VAE, which allows the authors to avoid the tricky problem of defining a distance on the space of graphs ... |
Pyqnc9eWhB | Graph Counterfactual Explainable AI via Latent Space Traversal | [
"Andreas Abildtrup Hansen",
"Paraskevas Pegios",
"Anna Calissano",
"Aasa Feragen"
] | Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. \textit{Counterfactual explanations} aim to explain predictions by finding the ``nearest'' in-distribution alternative input whose pre... | [
"Explainable AI",
"Counterfactual explanations",
"graph",
"equivariance",
"invariance",
"symmetry",
"VAE"
] | https://openreview.net/pdf?id=Pyqnc9eWhB | https://openreview.net/forum?id=Pyqnc9eWhB | N8P8Rr61AL | official_review | 1,728,296,097,434 | Pyqnc9eWhB | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission48/Reviewer_5Zk4"
] | NLDL.org/2025/Conference | 2025 | title: preliminary but sensible contribution
summary: The paper introduces a pipeline for generating (in-distribution) counterfactual
explanations of GNN decisions. An equivariant generative graph model is used
for the purpose. Specifically, the idea is to model a generative distribution
over in-distribution graphs a... |
Pyqnc9eWhB | Graph Counterfactual Explainable AI via Latent Space Traversal | [
"Andreas Abildtrup Hansen",
"Paraskevas Pegios",
"Anna Calissano",
"Aasa Feragen"
] | Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. \textit{Counterfactual explanations} aim to explain predictions by finding the ``nearest'' in-distribution alternative input whose pre... | [
"Explainable AI",
"Counterfactual explanations",
"graph",
"equivariance",
"invariance",
"symmetry",
"VAE"
] | https://openreview.net/pdf?id=Pyqnc9eWhB | https://openreview.net/forum?id=Pyqnc9eWhB | B3Ksp1fp5P | meta_review | 1,730,553,716,193 | Pyqnc9eWhB | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission48/Area_Chair_XwGk"
] | NLDL.org/2025/Conference | 2025 | metareview: The authors propose a method to generate counterfactual examples of GNN decisions by pushing the output to cross the decision boundary line. Nevertheless, the reviewers and the authors qualify the results as preliminary as the method could be improved.
I, therefore, recommend accepting the paper for a post... |
Pyqnc9eWhB | Graph Counterfactual Explainable AI via Latent Space Traversal | [
"Andreas Abildtrup Hansen",
"Paraskevas Pegios",
"Anna Calissano",
"Aasa Feragen"
] | Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. \textit{Counterfactual explanations} aim to explain predictions by finding the ``nearest'' in-distribution alternative input whose pre... | [
"Explainable AI",
"Counterfactual explanations",
"graph",
"equivariance",
"invariance",
"symmetry",
"VAE"
] | https://openreview.net/pdf?id=Pyqnc9eWhB | https://openreview.net/forum?id=Pyqnc9eWhB | 3hTcwyFAOd | official_review | 1,728,503,439,882 | Pyqnc9eWhB | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission48/Reviewer_BpLm"
] | NLDL.org/2025/Conference | 2025 | title: Review of paper 48
summary: The authors propose a method to generate semantically meaningful counterfactual explanations for graph classifiers by traversing the well regularised latent space learned by PEGVAE. The authors validate their approach and show the its effectiveness on three graph datasets.
strengths: ... |
PenPJYfmaA | NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks | [
"Bjørn Leth Møller",
"Sepideh Amiri",
"Christian Igel",
"Kristoffer Knutsen Wickstrøm",
"Robert Jenssen",
"Matthias Keicher",
"Mohammad Farid Azampour",
"Nassir Navab",
"Bulat Ibragimov"
] | A fundamental barrier to the adoption of AI systems in clinical practice is the insufficient transparency of AI decision-making. The field of Explainable Artificial Intelligence (XAI) seeks to provide human-interpretable explanations for a given AI model. The recently proposed Neural Explanation Mask (NEM) framework is... | [
"XAI",
"MIA"
] | https://openreview.net/pdf?id=PenPJYfmaA | https://openreview.net/forum?id=PenPJYfmaA | s34acmStoz | official_review | 1,727,791,596,782 | PenPJYfmaA | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission17/Reviewer_kbVf"
] | NLDL.org/2025/Conference | 2025 | title: Review of NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks
summary: The work introduces NEMt, a modification of the NEM method to specifically explain image classification predictions, in contrast to the original model which explains latent representations.
The work also ext... |
PenPJYfmaA | NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks | [
"Bjørn Leth Møller",
"Sepideh Amiri",
"Christian Igel",
"Kristoffer Knutsen Wickstrøm",
"Robert Jenssen",
"Matthias Keicher",
"Mohammad Farid Azampour",
"Nassir Navab",
"Bulat Ibragimov"
] | A fundamental barrier to the adoption of AI systems in clinical practice is the insufficient transparency of AI decision-making. The field of Explainable Artificial Intelligence (XAI) seeks to provide human-interpretable explanations for a given AI model. The recently proposed Neural Explanation Mask (NEM) framework is... | [
"XAI",
"MIA"
] | https://openreview.net/pdf?id=PenPJYfmaA | https://openreview.net/forum?id=PenPJYfmaA | SXG54uyicl | decision | 1,730,901,555,074 | PenPJYfmaA | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Oral)
comment: We recommend an oral and a poster presentation given the AC and reviewers recommendations. |
PenPJYfmaA | NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks | [
"Bjørn Leth Møller",
"Sepideh Amiri",
"Christian Igel",
"Kristoffer Knutsen Wickstrøm",
"Robert Jenssen",
"Matthias Keicher",
"Mohammad Farid Azampour",
"Nassir Navab",
"Bulat Ibragimov"
] | A fundamental barrier to the adoption of AI systems in clinical practice is the insufficient transparency of AI decision-making. The field of Explainable Artificial Intelligence (XAI) seeks to provide human-interpretable explanations for a given AI model. The recently proposed Neural Explanation Mask (NEM) framework is... | [
"XAI",
"MIA"
] | https://openreview.net/pdf?id=PenPJYfmaA | https://openreview.net/forum?id=PenPJYfmaA | K85z6guUpR | official_review | 1,728,415,394,183 | PenPJYfmaA | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission17/Reviewer_z9b3"
] | NLDL.org/2025/Conference | 2025 | title: Organized paper that expands on existing XAI framework and shows results on two common medical imaging datasets
summary: The paper proposes Neural Explanation Masks with target labels (NEMt), an XAI method which is a variant of the Neural Explanation Mask (NEM) framework. The NEM framework trains an explanation ... |
PenPJYfmaA | NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks | [
"Bjørn Leth Møller",
"Sepideh Amiri",
"Christian Igel",
"Kristoffer Knutsen Wickstrøm",
"Robert Jenssen",
"Matthias Keicher",
"Mohammad Farid Azampour",
"Nassir Navab",
"Bulat Ibragimov"
] | A fundamental barrier to the adoption of AI systems in clinical practice is the insufficient transparency of AI decision-making. The field of Explainable Artificial Intelligence (XAI) seeks to provide human-interpretable explanations for a given AI model. The recently proposed Neural Explanation Mask (NEM) framework is... | [
"XAI",
"MIA"
] | https://openreview.net/pdf?id=PenPJYfmaA | https://openreview.net/forum?id=PenPJYfmaA | HnvaSje4ra | official_review | 1,728,235,032,767 | PenPJYfmaA | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission17/Reviewer_iqmS"
] | NLDL.org/2025/Conference | 2025 | title: An interesting XAI module for artificial neural networks
summary: The paper proposes a variant to a recently introduced Neural Explanation Mask (NEM) framework, called NEMt, where the additional "t" in the name corresponds to the notion of "target labels". The idea is that the original NEM framework works in an... |
PenPJYfmaA | NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks | [
"Bjørn Leth Møller",
"Sepideh Amiri",
"Christian Igel",
"Kristoffer Knutsen Wickstrøm",
"Robert Jenssen",
"Matthias Keicher",
"Mohammad Farid Azampour",
"Nassir Navab",
"Bulat Ibragimov"
] | A fundamental barrier to the adoption of AI systems in clinical practice is the insufficient transparency of AI decision-making. The field of Explainable Artificial Intelligence (XAI) seeks to provide human-interpretable explanations for a given AI model. The recently proposed Neural Explanation Mask (NEM) framework is... | [
"XAI",
"MIA"
] | https://openreview.net/pdf?id=PenPJYfmaA | https://openreview.net/forum?id=PenPJYfmaA | CUfaKDL1KD | meta_review | 1,730,631,839,286 | PenPJYfmaA | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission17/Area_Chair_eGap"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper proposes a method called Neural Explanation Masks with target labels (NEMt), an extension of the existing NEM framework tailored for supervised settings to improve interpretability for neural nets. The method addresses a weakness of the existing NEM and optimizes for target labels, incorporates a ... |
Pb47B5t0pr | PePR: Performance Per Resource Unit as a Metric to Promote Small-scale Deep Learning in Medical Image Analysis | [
"Raghavendra Selvan",
"Bob Pepin",
"Christian Igel",
"Gabrielle Samuel",
"Erik B Dam"
] | The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry ba... | [
"Equitable AI",
"Resource efficiency",
"Image Classification",
"Deep Learning"
] | https://openreview.net/pdf?id=Pb47B5t0pr | https://openreview.net/forum?id=Pb47B5t0pr | qdEB4to0iw | official_review | 1,728,481,478,194 | Pb47B5t0pr | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission23/Reviewer_WeNX"
] | NLDL.org/2025/Conference | 2025 | title: Review of PePR: Performance Per Resource Unit as a Metric to Promote Small-scale Deep Learning
summary: In this work, the authors propose a metric called PePR, which is a measure of performance per resource-unit and use it to try to show that small-scale Deep Learning has a better trade-off of performance to res... |
Pb47B5t0pr | PePR: Performance Per Resource Unit as a Metric to Promote Small-scale Deep Learning in Medical Image Analysis | [
"Raghavendra Selvan",
"Bob Pepin",
"Christian Igel",
"Gabrielle Samuel",
"Erik B Dam"
] | The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry ba... | [
"Equitable AI",
"Resource efficiency",
"Image Classification",
"Deep Learning"
] | https://openreview.net/pdf?id=Pb47B5t0pr | https://openreview.net/forum?id=Pb47B5t0pr | dMLwIHp7pl | official_review | 1,728,262,748,760 | Pb47B5t0pr | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission23/Reviewer_HFyD"
] | NLDL.org/2025/Conference | 2025 | title: I would suggest major revisions before acceptance.
summary: The paper introduces the PePR score, a novel metric that measures performance per resource unit for deep learning (DL) models, with a focus on vision tasks, particularly in resource-constrained settings like medical imaging. The authors argue that large... |
Pb47B5t0pr | PePR: Performance Per Resource Unit as a Metric to Promote Small-scale Deep Learning in Medical Image Analysis | [
"Raghavendra Selvan",
"Bob Pepin",
"Christian Igel",
"Gabrielle Samuel",
"Erik B Dam"
] | The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry ba... | [
"Equitable AI",
"Resource efficiency",
"Image Classification",
"Deep Learning"
] | https://openreview.net/pdf?id=Pb47B5t0pr | https://openreview.net/forum?id=Pb47B5t0pr | QXQA65p58j | official_review | 1,727,470,557,745 | Pb47B5t0pr | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission23/Reviewer_5oEE"
] | NLDL.org/2025/Conference | 2025 | title: Official Review
summary: The paper titled introduces the PePR score (Performance per Resource Unit) as a novel metric to evaluate deep learning (DL) models, particularly in resource-constrained settings like the Global South. The research focuses on balancing model performance with resource consumption (e.g., co... |
Pb47B5t0pr | PePR: Performance Per Resource Unit as a Metric to Promote Small-scale Deep Learning in Medical Image Analysis | [
"Raghavendra Selvan",
"Bob Pepin",
"Christian Igel",
"Gabrielle Samuel",
"Erik B Dam"
] | The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry ba... | [
"Equitable AI",
"Resource efficiency",
"Image Classification",
"Deep Learning"
] | https://openreview.net/pdf?id=Pb47B5t0pr | https://openreview.net/forum?id=Pb47B5t0pr | DRPuLs5R5R | meta_review | 1,730,473,058,882 | Pb47B5t0pr | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission23/Area_Chair_rJAR"
] | NLDL.org/2025/Conference | 2025 | metareview: This paper introduces the PePR score, a metric designed to evaluate the trade-off between performance and resource consumption in deep learning models, with a particular focus on resource-constrained environments like medical imaging. The study is timely, tackling AI equity concerns by highlighting the reso... |
Pb47B5t0pr | PePR: Performance Per Resource Unit as a Metric to Promote Small-scale Deep Learning in Medical Image Analysis | [
"Raghavendra Selvan",
"Bob Pepin",
"Christian Igel",
"Gabrielle Samuel",
"Erik B Dam"
] | The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry ba... | [
"Equitable AI",
"Resource efficiency",
"Image Classification",
"Deep Learning"
] | https://openreview.net/pdf?id=Pb47B5t0pr | https://openreview.net/forum?id=Pb47B5t0pr | 6QHSqwqFyM | decision | 1,730,901,555,403 | Pb47B5t0pr | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Oral)
comment: We recommend an oral and a poster presentation given the AC and reviewers recommendations. |
Pb47B5t0pr | PePR: Performance Per Resource Unit as a Metric to Promote Small-scale Deep Learning in Medical Image Analysis | [
"Raghavendra Selvan",
"Bob Pepin",
"Christian Igel",
"Gabrielle Samuel",
"Erik B Dam"
] | The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry ba... | [
"Equitable AI",
"Resource efficiency",
"Image Classification",
"Deep Learning"
] | https://openreview.net/pdf?id=Pb47B5t0pr | https://openreview.net/forum?id=Pb47B5t0pr | 4mqYkXM33l | official_review | 1,728,085,110,760 | Pb47B5t0pr | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission23/Reviewer_uyFP"
] | NLDL.org/2025/Conference | 2025 | title: Interesting problem, but more results would improve the clarity of the paper
summary: This paper discusses the performance-resource tradeoff in small- and large-scale DL models. The authors shed light on the inaccessibility to large-scale computing and data, which hinders researchers' ability, especially in the ... |
M5hbCbJs8R | DP-KAN: Differentially Private Kolmogorov-Arnold Networks | [] | We study the Kolmogorov-Arnold Network (KAN), recently proposed as an alternative to the classical Multilayer Perceptron (MLP), in the application for differentially private model training. Using the DP-SGD algorithm, we demonstrate that KAN can be made private in a straightforward manner and evaluated its performance ... | [
"Kolmogorov-Arnold Networks",
"Differential Privacy"
] | https://openreview.net/pdf?id=M5hbCbJs8R | https://openreview.net/forum?id=M5hbCbJs8R | xDPCIkHd0D | official_review | 1,728,295,617,673 | M5hbCbJs8R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission13/Reviewer_VhGM"
] | NLDL.org/2025/Conference | 2025 | title: Experimental results on DP-SGD in KAN are interesting, but questions remain
summary: The paper studies the use of differentially private SGD on Kolmogorov-Arnold networks and compare results to MLPs and linear regression. The results are, while not surprising, of practical interest but questions remain about the... |
M5hbCbJs8R | DP-KAN: Differentially Private Kolmogorov-Arnold Networks | [] | We study the Kolmogorov-Arnold Network (KAN), recently proposed as an alternative to the classical Multilayer Perceptron (MLP), in the application for differentially private model training. Using the DP-SGD algorithm, we demonstrate that KAN can be made private in a straightforward manner and evaluated its performance ... | [
"Kolmogorov-Arnold Networks",
"Differential Privacy"
] | https://openreview.net/pdf?id=M5hbCbJs8R | https://openreview.net/forum?id=M5hbCbJs8R | rjLcwu4zAR | official_review | 1,728,458,294,473 | M5hbCbJs8R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission13/Reviewer_D8gj"
] | NLDL.org/2025/Conference | 2025 | title: An empirical comparison between privacy-preserving training of MLPs and KANs
summary: This paper studies training of Kolmogorov-Arnold Networks (KANs) under the privacy protection by differential privacy (DP). DP is known to degrade the utility of the learning in order to protect the privacy of the individuals. ... |
M5hbCbJs8R | DP-KAN: Differentially Private Kolmogorov-Arnold Networks | [] | We study the Kolmogorov-Arnold Network (KAN), recently proposed as an alternative to the classical Multilayer Perceptron (MLP), in the application for differentially private model training. Using the DP-SGD algorithm, we demonstrate that KAN can be made private in a straightforward manner and evaluated its performance ... | [
"Kolmogorov-Arnold Networks",
"Differential Privacy"
] | https://openreview.net/pdf?id=M5hbCbJs8R | https://openreview.net/forum?id=M5hbCbJs8R | oJ2fuO9SCj | decision | 1,730,901,554,743 | M5hbCbJs8R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Reject |
M5hbCbJs8R | DP-KAN: Differentially Private Kolmogorov-Arnold Networks | [] | We study the Kolmogorov-Arnold Network (KAN), recently proposed as an alternative to the classical Multilayer Perceptron (MLP), in the application for differentially private model training. Using the DP-SGD algorithm, we demonstrate that KAN can be made private in a straightforward manner and evaluated its performance ... | [
"Kolmogorov-Arnold Networks",
"Differential Privacy"
] | https://openreview.net/pdf?id=M5hbCbJs8R | https://openreview.net/forum?id=M5hbCbJs8R | U6q8JghCah | official_review | 1,726,973,346,304 | M5hbCbJs8R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission13/Reviewer_SnD9"
] | NLDL.org/2025/Conference | 2025 | title: Review for DP-KAN: Differentially Private Kolmogorov-Arnold Networks
summary: This study explores the Kolmogorov-Arnold Network (KAN) as an alternative to the classical Multilayer Perceptron (MLP) for differentially private model training. Using the DP-SGD algorithm, KAN was made differentially private and evalu... |
M5hbCbJs8R | DP-KAN: Differentially Private Kolmogorov-Arnold Networks | [] | We study the Kolmogorov-Arnold Network (KAN), recently proposed as an alternative to the classical Multilayer Perceptron (MLP), in the application for differentially private model training. Using the DP-SGD algorithm, we demonstrate that KAN can be made private in a straightforward manner and evaluated its performance ... | [
"Kolmogorov-Arnold Networks",
"Differential Privacy"
] | https://openreview.net/pdf?id=M5hbCbJs8R | https://openreview.net/forum?id=M5hbCbJs8R | A1VRIgkafe | meta_review | 1,730,317,068,245 | M5hbCbJs8R | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission13/Area_Chair_ibJ4"
] | NLDL.org/2025/Conference | 2025 | metareview: The authors present privacy-aware Kolmogorov-Arnold Networks (KANs), i.e. KANs trained with differential privacy, ensuring privacy without compromising accuracy. KANs exhibit similar accuracy degradation to MLPs when trained with differential privacy, making them a promising option for privacy-preserving mo... |
KcBMGkip79 | Bounds on the Generalization Error in Active Learning | [
"Vincent Menden",
"Yahya Saleh",
"Armin Iske"
] | We establish empirical risk minimization principles for active learning by
deriving a family of upper bounds on the generalization error. Aligning with empirical observations, the bounds suggest that superior query algorithms can be obtained by
combining both informativeness and representativeness query strategies, wh... | [
"Active Learning",
"Empirical Risk Minimization Principle",
"Integral Probability Metric"
] | https://openreview.net/pdf?id=KcBMGkip79 | https://openreview.net/forum?id=KcBMGkip79 | qnEdU49c7J | official_review | 1,728,472,669,276 | KcBMGkip79 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission18/Reviewer_8tjT"
] | NLDL.org/2025/Conference | 2025 | title: Assessment of Theoretical Contributions and Practical Implications
summary: The paper derives a family of upper bounds on generalization error in the context of active learning (AL). It combines query strategies based on informativeness and representativeness to optimize the selection of data points for labeling... |
KcBMGkip79 | Bounds on the Generalization Error in Active Learning | [
"Vincent Menden",
"Yahya Saleh",
"Armin Iske"
] | We establish empirical risk minimization principles for active learning by
deriving a family of upper bounds on the generalization error. Aligning with empirical observations, the bounds suggest that superior query algorithms can be obtained by
combining both informativeness and representativeness query strategies, wh... | [
"Active Learning",
"Empirical Risk Minimization Principle",
"Integral Probability Metric"
] | https://openreview.net/pdf?id=KcBMGkip79 | https://openreview.net/forum?id=KcBMGkip79 | TOPiaZZEbc | official_review | 1,728,320,597,498 | KcBMGkip79 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission18/Reviewer_6img"
] | NLDL.org/2025/Conference | 2025 | title: Interesting math, potentially good information, lackluster communication
summary: This paper presents a theoretically derived upper bound on generalization risk in active learning environments. The authors provide mathematical context and justification for their upper bound, then provide examples by applying th... |
KcBMGkip79 | Bounds on the Generalization Error in Active Learning | [
"Vincent Menden",
"Yahya Saleh",
"Armin Iske"
] | We establish empirical risk minimization principles for active learning by
deriving a family of upper bounds on the generalization error. Aligning with empirical observations, the bounds suggest that superior query algorithms can be obtained by
combining both informativeness and representativeness query strategies, wh... | [
"Active Learning",
"Empirical Risk Minimization Principle",
"Integral Probability Metric"
] | https://openreview.net/pdf?id=KcBMGkip79 | https://openreview.net/forum?id=KcBMGkip79 | KWJbsd7oBa | official_review | 1,728,233,708,628 | KcBMGkip79 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission18/Reviewer_jtnw"
] | NLDL.org/2025/Conference | 2025 | title: IPM based ERM analysis in Active Learning
summary: The paper aims to understand active learning by deriving new upper bounds on the generalization error. It introduces Integral Probability Metrics (IPMs) to measure how well the selected samples represent the overall data distribution. The key idea is that active... |
KcBMGkip79 | Bounds on the Generalization Error in Active Learning | [
"Vincent Menden",
"Yahya Saleh",
"Armin Iske"
] | We establish empirical risk minimization principles for active learning by
deriving a family of upper bounds on the generalization error. Aligning with empirical observations, the bounds suggest that superior query algorithms can be obtained by
combining both informativeness and representativeness query strategies, wh... | [
"Active Learning",
"Empirical Risk Minimization Principle",
"Integral Probability Metric"
] | https://openreview.net/pdf?id=KcBMGkip79 | https://openreview.net/forum?id=KcBMGkip79 | ChKLqmglNj | meta_review | 1,730,421,121,801 | KcBMGkip79 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission18/Area_Chair_mwN2"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper established ERM generalization bounds for active learning, based on Integral Probability Metrics. Some issues were raised by reviewers, mostly related to lack of direct applications of the proposed theoretical framework to concrete algorithms. Yet, the derivation of the proposed bound is meaningfu... |
KcBMGkip79 | Bounds on the Generalization Error in Active Learning | [
"Vincent Menden",
"Yahya Saleh",
"Armin Iske"
] | We establish empirical risk minimization principles for active learning by
deriving a family of upper bounds on the generalization error. Aligning with empirical observations, the bounds suggest that superior query algorithms can be obtained by
combining both informativeness and representativeness query strategies, wh... | [
"Active Learning",
"Empirical Risk Minimization Principle",
"Integral Probability Metric"
] | https://openreview.net/pdf?id=KcBMGkip79 | https://openreview.net/forum?id=KcBMGkip79 | Ath6ovAO5k | decision | 1,730,901,555,229 | KcBMGkip79 | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Poster)
comment: We recommend a poster presentation given the AC and reviewers recommendations. |
KaZzDtUeJY | Predicting Oligomeric states of Fluorescent Proteins using Mamba | [
"Agney K Rajeev",
"Joel Joseph K B",
"Subhankar Mishra"
] | Fluorescent proteins (FPs) are essential tools in biomedical imaging, known for their ability to absorb and emit light, thereby allowing visualization of biological processes. Understanding the oligomeric state is crucial, as monomeric forms are often preferred in applications to minimize potential artifacts and preven... | [
"Mamba",
"Fluorescent proteins",
"Oligomerization state"
] | https://openreview.net/pdf?id=KaZzDtUeJY | https://openreview.net/forum?id=KaZzDtUeJY | ycIbYbIl5N | official_review | 1,728,992,720,349 | KaZzDtUeJY | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission50/Reviewer_m2uG"
] | NLDL.org/2025/Conference | 2025 | title: Promising Approach with Mamba Architecture for Predicting Protein Oligomeric States, but Lacks Key Comparisons and Clarity
summary: This paper proposes a method to predict the oligomeric state of fluorescent proteins using a deep learning model based on the Mamba architecture. The authors apply different data au... |
KaZzDtUeJY | Predicting Oligomeric states of Fluorescent Proteins using Mamba | [
"Agney K Rajeev",
"Joel Joseph K B",
"Subhankar Mishra"
] | Fluorescent proteins (FPs) are essential tools in biomedical imaging, known for their ability to absorb and emit light, thereby allowing visualization of biological processes. Understanding the oligomeric state is crucial, as monomeric forms are often preferred in applications to minimize potential artifacts and preven... | [
"Mamba",
"Fluorescent proteins",
"Oligomerization state"
] | https://openreview.net/pdf?id=KaZzDtUeJY | https://openreview.net/forum?id=KaZzDtUeJY | loMI5CxzdX | decision | 1,730,901,556,636 | KaZzDtUeJY | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Oral)
comment: We recommend an oral and a poster presentation given the AC and reviewers recommendations. |
KaZzDtUeJY | Predicting Oligomeric states of Fluorescent Proteins using Mamba | [
"Agney K Rajeev",
"Joel Joseph K B",
"Subhankar Mishra"
] | Fluorescent proteins (FPs) are essential tools in biomedical imaging, known for their ability to absorb and emit light, thereby allowing visualization of biological processes. Understanding the oligomeric state is crucial, as monomeric forms are often preferred in applications to minimize potential artifacts and preven... | [
"Mamba",
"Fluorescent proteins",
"Oligomerization state"
] | https://openreview.net/pdf?id=KaZzDtUeJY | https://openreview.net/forum?id=KaZzDtUeJY | VVDQWZhIcJ | official_review | 1,728,910,223,572 | KaZzDtUeJY | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission50/Reviewer_hT39"
] | NLDL.org/2025/Conference | 2025 | title: Review of the paper "Predicting Oligomeric states of Fluorescent Proteins using Mamba"
summary: This paper presents a method for categorizing the oligomeric states of proteins based on their amino acid sequences. The novelty of the research lies in its use of deep learning frameworks, particularly the recently d... |
KaZzDtUeJY | Predicting Oligomeric states of Fluorescent Proteins using Mamba | [
"Agney K Rajeev",
"Joel Joseph K B",
"Subhankar Mishra"
] | Fluorescent proteins (FPs) are essential tools in biomedical imaging, known for their ability to absorb and emit light, thereby allowing visualization of biological processes. Understanding the oligomeric state is crucial, as monomeric forms are often preferred in applications to minimize potential artifacts and preven... | [
"Mamba",
"Fluorescent proteins",
"Oligomerization state"
] | https://openreview.net/pdf?id=KaZzDtUeJY | https://openreview.net/forum?id=KaZzDtUeJY | R6q02yJgm9 | official_review | 1,728,517,632,425 | KaZzDtUeJY | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission50/Reviewer_KfRX"
] | NLDL.org/2025/Conference | 2025 | title: A neural network approach to predict state of fluorescnet proteins such as GFP and dsRed.
summary: Fluorophores used in biomedical imaging may consist of a single fluorescing unit, or a cluster of several units, influencing their ability to provide a useful signal. This paper investigates the possibility to pred... |
KaZzDtUeJY | Predicting Oligomeric states of Fluorescent Proteins using Mamba | [
"Agney K Rajeev",
"Joel Joseph K B",
"Subhankar Mishra"
] | Fluorescent proteins (FPs) are essential tools in biomedical imaging, known for their ability to absorb and emit light, thereby allowing visualization of biological processes. Understanding the oligomeric state is crucial, as monomeric forms are often preferred in applications to minimize potential artifacts and preven... | [
"Mamba",
"Fluorescent proteins",
"Oligomerization state"
] | https://openreview.net/pdf?id=KaZzDtUeJY | https://openreview.net/forum?id=KaZzDtUeJY | Ew1lXBpYVQ | official_review | 1,728,508,761,932 | KaZzDtUeJY | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission50/Reviewer_yi5B"
] | NLDL.org/2025/Conference | 2025 | title: Predicting Oligomeric states of Fluorescent Proteins using Mamba
summary: This paper test the new Mamba architecture to predict (classify) whether a fluorescent protein's structure will be monomeric or oligomeric based on the amino acid sequence. Two (standard) datasets are used, created and curated by others. T... |
KaZzDtUeJY | Predicting Oligomeric states of Fluorescent Proteins using Mamba | [
"Agney K Rajeev",
"Joel Joseph K B",
"Subhankar Mishra"
] | Fluorescent proteins (FPs) are essential tools in biomedical imaging, known for their ability to absorb and emit light, thereby allowing visualization of biological processes. Understanding the oligomeric state is crucial, as monomeric forms are often preferred in applications to minimize potential artifacts and preven... | [
"Mamba",
"Fluorescent proteins",
"Oligomerization state"
] | https://openreview.net/pdf?id=KaZzDtUeJY | https://openreview.net/forum?id=KaZzDtUeJY | 6LWowT7bRK | meta_review | 1,730,561,477,554 | KaZzDtUeJY | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission50/Area_Chair_4qBe"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper proposes a method using the Mamba deep learning architecture to predict the oligomeric state (monomeric vs. oligomeric) of fluorescent proteins based on their amino acid sequences. The model is tested for accuracy, sensitivity, and computational efficiency against traditional methods, including RN... |
Juf8b4be1Z | Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound | [] | Despite the rapid development of AI models in medical image analysis, their validation in real world clinical settings remains limited. Models are often developed without continuous feedback from clinicians, which can lead to a lack of alignment with the actual needs. To address this, we introduce a generic framework d... | [
"deployment",
"fetal ultrasound",
"standard plane detection"
] | https://openreview.net/pdf?id=Juf8b4be1Z | https://openreview.net/forum?id=Juf8b4be1Z | x4Jn1UwqBY | official_review | 1,727,957,411,290 | Juf8b4be1Z | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission44/Reviewer_z8CZ"
] | NLDL.org/2025/Conference | 2025 | title: Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
summary: The paper introduces a framework for the real-world deployment of deep learning models in medical workflows. The aim is to facilitate real-world clinical validation of AI in medical imaging in clinical... |
Juf8b4be1Z | Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound | [] | Despite the rapid development of AI models in medical image analysis, their validation in real world clinical settings remains limited. Models are often developed without continuous feedback from clinicians, which can lead to a lack of alignment with the actual needs. To address this, we introduce a generic framework d... | [
"deployment",
"fetal ultrasound",
"standard plane detection"
] | https://openreview.net/pdf?id=Juf8b4be1Z | https://openreview.net/forum?id=Juf8b4be1Z | vbLqGN9Nvb | official_review | 1,727,355,223,250 | Juf8b4be1Z | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission44/Reviewer_y175"
] | NLDL.org/2025/Conference | 2025 | title: Unsystematic collection of results, possibly out of scope, unclear definition of what is meant by a framework, no references to approvals
summary: They present a framework for early testing of visual AI-tools in a clinical context and report on an experiment that applies the framework in a fetal ultrasound setti... |
Juf8b4be1Z | Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound | [] | Despite the rapid development of AI models in medical image analysis, their validation in real world clinical settings remains limited. Models are often developed without continuous feedback from clinicians, which can lead to a lack of alignment with the actual needs. To address this, we introduce a generic framework d... | [
"deployment",
"fetal ultrasound",
"standard plane detection"
] | https://openreview.net/pdf?id=Juf8b4be1Z | https://openreview.net/forum?id=Juf8b4be1Z | r00FAiRL5u | decision | 1,730,901,556,453 | Juf8b4be1Z | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Reject |
Juf8b4be1Z | Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound | [] | Despite the rapid development of AI models in medical image analysis, their validation in real world clinical settings remains limited. Models are often developed without continuous feedback from clinicians, which can lead to a lack of alignment with the actual needs. To address this, we introduce a generic framework d... | [
"deployment",
"fetal ultrasound",
"standard plane detection"
] | https://openreview.net/pdf?id=Juf8b4be1Z | https://openreview.net/forum?id=Juf8b4be1Z | j4ZLMOWEoi | official_review | 1,727,856,282,380 | Juf8b4be1Z | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission44/Reviewer_L2uz"
] | NLDL.org/2025/Conference | 2025 | title: Promising research with room for additional detail
summary: The paper presents a framework for deploying a generic deep learning model, providing a case study in clinical obstetric ultrasound settings. It focuses on real-time video processing for enhancing ultrasound imaging through artificial intelligence. The ... |
Juf8b4be1Z | Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound | [] | Despite the rapid development of AI models in medical image analysis, their validation in real world clinical settings remains limited. Models are often developed without continuous feedback from clinicians, which can lead to a lack of alignment with the actual needs. To address this, we introduce a generic framework d... | [
"deployment",
"fetal ultrasound",
"standard plane detection"
] | https://openreview.net/pdf?id=Juf8b4be1Z | https://openreview.net/forum?id=Juf8b4be1Z | DGUQaKl49S | official_review | 1,728,508,205,656 | Juf8b4be1Z | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission44/Reviewer_BEct"
] | NLDL.org/2025/Conference | 2025 | title: Framework for running AI models for ultrasound in clinical environment
summary: The paper presents a computational framework for running and assessing AI models for ultrasound images, lowering the barrier for deployment of AI in clinical use. The authors use obstetric ultrasound as a case study to demonstrate th... |
Juf8b4be1Z | Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound | [] | Despite the rapid development of AI models in medical image analysis, their validation in real world clinical settings remains limited. Models are often developed without continuous feedback from clinicians, which can lead to a lack of alignment with the actual needs. To address this, we introduce a generic framework d... | [
"deployment",
"fetal ultrasound",
"standard plane detection"
] | https://openreview.net/pdf?id=Juf8b4be1Z | https://openreview.net/forum?id=Juf8b4be1Z | 9eaDfciFUO | meta_review | 1,730,663,360,286 | Juf8b4be1Z | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission44/Area_Chair_zKW7"
] | NLDL.org/2025/Conference | 2025 | metareview: The submitted paper proposes a framework for deploying AI models in clinical routine. The presented framework was used for a case study in which a AI-based standard plane detection model for fetal ultrasound was deployed and tested by clinicians. All reviewers acknowledged that the translation of AI models ... |
JNxddbPPWt | Locally orderless networks | [
"Jon Sporring",
"Peidi Xu",
"Jiahao Lu",
"Francois Bernard Lauze",
"Sune Darkner"
] | We present Locally Orderless Networks (LON) and the theoretical foundation that links them to Convolutional Neural Networks (CNN), Scale-space histograms, and measurement theory. The key elements are a regular sampling of the bias and the derivative of the activation function. We compare LON, CNN, and Scale-space histo... | [
"Convolutional Neural Networks",
"Locally Orderless Images",
"histograms",
"saliency maps",
"explainability"
] | https://openreview.net/pdf?id=JNxddbPPWt | https://openreview.net/forum?id=JNxddbPPWt | j1dFWdSVGM | decision | 1,730,901,556,344 | JNxddbPPWt | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Oral)
comment: Given the AC positive recommendation, we recommend an oral and a poster presentation given the AC and reviewers recommendations. |
JNxddbPPWt | Locally orderless networks | [
"Jon Sporring",
"Peidi Xu",
"Jiahao Lu",
"Francois Bernard Lauze",
"Sune Darkner"
] | We present Locally Orderless Networks (LON) and the theoretical foundation that links them to Convolutional Neural Networks (CNN), Scale-space histograms, and measurement theory. The key elements are a regular sampling of the bias and the derivative of the activation function. We compare LON, CNN, and Scale-space histo... | [
"Convolutional Neural Networks",
"Locally Orderless Images",
"histograms",
"saliency maps",
"explainability"
] | https://openreview.net/pdf?id=JNxddbPPWt | https://openreview.net/forum?id=JNxddbPPWt | hbvNUD3mL5 | official_review | 1,728,466,698,900 | JNxddbPPWt | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission40/Reviewer_qFuz"
] | NLDL.org/2025/Conference | 2025 | title: Locally Orderless Networks
summary: The paper proposes an architecture called "Locally Orderless Networks" (LONs), which is analogous to CNNs with biases but uses Gaussian functions as activations. The authors note that sigmoid functions, which are common activations in CNNs, can be viewed as integrals of Gaussi... |
JNxddbPPWt | Locally orderless networks | [
"Jon Sporring",
"Peidi Xu",
"Jiahao Lu",
"Francois Bernard Lauze",
"Sune Darkner"
] | We present Locally Orderless Networks (LON) and the theoretical foundation that links them to Convolutional Neural Networks (CNN), Scale-space histograms, and measurement theory. The key elements are a regular sampling of the bias and the derivative of the activation function. We compare LON, CNN, and Scale-space histo... | [
"Convolutional Neural Networks",
"Locally Orderless Images",
"histograms",
"saliency maps",
"explainability"
] | https://openreview.net/pdf?id=JNxddbPPWt | https://openreview.net/forum?id=JNxddbPPWt | WNOod92mUV | meta_review | 1,730,494,424,283 | JNxddbPPWt | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission40/Area_Chair_h5iB"
] | NLDL.org/2025/Conference | 2025 | metareview: The reviewers agree that the paper is well-motivated and has a good theoretical foundation. Reviewers, however, also differ in their opinions, with two positive reviewers, one neutral, and one that recommends a rejection. The strong aspects of the paper are its theoretical foundation, that it is based on a ... |
JNxddbPPWt | Locally orderless networks | [
"Jon Sporring",
"Peidi Xu",
"Jiahao Lu",
"Francois Bernard Lauze",
"Sune Darkner"
] | We present Locally Orderless Networks (LON) and the theoretical foundation that links them to Convolutional Neural Networks (CNN), Scale-space histograms, and measurement theory. The key elements are a regular sampling of the bias and the derivative of the activation function. We compare LON, CNN, and Scale-space histo... | [
"Convolutional Neural Networks",
"Locally Orderless Images",
"histograms",
"saliency maps",
"explainability"
] | https://openreview.net/pdf?id=JNxddbPPWt | https://openreview.net/forum?id=JNxddbPPWt | UCqYETgaGJ | official_review | 1,728,458,560,783 | JNxddbPPWt | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission40/Reviewer_rzBp"
] | NLDL.org/2025/Conference | 2025 | title: Locally Orderless Networks as a generalization of Convolutional Neural Networks
summary: This paper introduces a novel neural network layer called Locally Orderless Networks (LON) and explores its theoretical connections with Convolutional Neural Networks (CNN) and scale-space histograms. The core of LON lies i... |
JNxddbPPWt | Locally orderless networks | [
"Jon Sporring",
"Peidi Xu",
"Jiahao Lu",
"Francois Bernard Lauze",
"Sune Darkner"
] | We present Locally Orderless Networks (LON) and the theoretical foundation that links them to Convolutional Neural Networks (CNN), Scale-space histograms, and measurement theory. The key elements are a regular sampling of the bias and the derivative of the activation function. We compare LON, CNN, and Scale-space histo... | [
"Convolutional Neural Networks",
"Locally Orderless Images",
"histograms",
"saliency maps",
"explainability"
] | https://openreview.net/pdf?id=JNxddbPPWt | https://openreview.net/forum?id=JNxddbPPWt | KUqw9swSGw | official_review | 1,727,315,763,437 | JNxddbPPWt | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission40/Reviewer_cJRh"
] | NLDL.org/2025/Conference | 2025 | title: Locally Orderless Networks - Interesting approach with narrow / limited results
summary: The article introduces Locally Orderless Networks; operators with roots in Locally Orderless Image frameworks. The work draws on kernel methods via general Parzen-Rosenblatt windows and local histograms, and connect these to... |
JNxddbPPWt | Locally orderless networks | [
"Jon Sporring",
"Peidi Xu",
"Jiahao Lu",
"Francois Bernard Lauze",
"Sune Darkner"
] | We present Locally Orderless Networks (LON) and the theoretical foundation that links them to Convolutional Neural Networks (CNN), Scale-space histograms, and measurement theory. The key elements are a regular sampling of the bias and the derivative of the activation function. We compare LON, CNN, and Scale-space histo... | [
"Convolutional Neural Networks",
"Locally Orderless Images",
"histograms",
"saliency maps",
"explainability"
] | https://openreview.net/pdf?id=JNxddbPPWt | https://openreview.net/forum?id=JNxddbPPWt | 6OhEx0v5aM | official_review | 1,728,464,335,317 | JNxddbPPWt | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission40/Reviewer_xnJP"
] | NLDL.org/2025/Conference | 2025 | title: Locally Orderless Networks - Strengths in theoretical foundations and weaknesses in experimental scope and generalizability
summary: The paper "Locally Orderless Networks" (LONs) introduces a novel neural network architecture that integrates scale-space theory and measure theory, enhancing the computational capa... |
JBH3mtjG9I | FreqRISE: Explaining time series using frequency masking | [
"Thea Brüsch",
"Kristoffer Knutsen Wickstrøm",
"Mikkel N. Schmidt",
"Tommy Sonne Alstrøm",
"Robert Jenssen"
] | Time series data is fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. ... | [
"Explainability",
"Time series data",
"Audio data"
] | https://openreview.net/pdf?id=JBH3mtjG9I | https://openreview.net/forum?id=JBH3mtjG9I | u3OC3LdQZr | official_review | 1,728,295,319,648 | JBH3mtjG9I | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission11/Reviewer_Bf1T"
] | NLDL.org/2025/Conference | 2025 | title: Promising concept but critical gaps in analysis and discussion
summary: The authors introduce FreqRISE, an extension of the RISE explainability method applied to time series data by transforming it into the frequency and time-frequency domains. FreqRISE outperforms baseline methods in identifying relevant featur... |
JBH3mtjG9I | FreqRISE: Explaining time series using frequency masking | [
"Thea Brüsch",
"Kristoffer Knutsen Wickstrøm",
"Mikkel N. Schmidt",
"Tommy Sonne Alstrøm",
"Robert Jenssen"
] | Time series data is fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. ... | [
"Explainability",
"Time series data",
"Audio data"
] | https://openreview.net/pdf?id=JBH3mtjG9I | https://openreview.net/forum?id=JBH3mtjG9I | rqvju5ieTM | meta_review | 1,730,500,304,919 | JBH3mtjG9I | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission11/Area_Chair_kTGG"
] | NLDL.org/2025/Conference | 2025 | metareview: The paper presents a new way to do XAI in the frequency domain of models which classify time-series.
The idea is potentially novel but the presentation is significantly unclear.
The basic definition given in equation 3 is mathematically quite unclear,
- even if one tries to match this against the RISE pape... |
JBH3mtjG9I | FreqRISE: Explaining time series using frequency masking | [
"Thea Brüsch",
"Kristoffer Knutsen Wickstrøm",
"Mikkel N. Schmidt",
"Tommy Sonne Alstrøm",
"Robert Jenssen"
] | Time series data is fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. ... | [
"Explainability",
"Time series data",
"Audio data"
] | https://openreview.net/pdf?id=JBH3mtjG9I | https://openreview.net/forum?id=JBH3mtjG9I | gzDJAkArk9 | official_review | 1,728,053,115,234 | JBH3mtjG9I | [
"everyone"
] | [
"NLDL.org/2025/Conference/Submission11/Reviewer_fUhF"
] | NLDL.org/2025/Conference | 2025 | title: Introduction of a new explanation method for time series data
summary: This paper builds upon existing explanation methods for time series data which utilize masking (RISE) and introduces the idea of applying masking to the frequency domain rather than input space. The outputs are then transformed back to the in... |
JBH3mtjG9I | FreqRISE: Explaining time series using frequency masking | [
"Thea Brüsch",
"Kristoffer Knutsen Wickstrøm",
"Mikkel N. Schmidt",
"Tommy Sonne Alstrøm",
"Robert Jenssen"
] | Time series data is fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop explainable artificial intelligence in these domains therefore implies explaining salient information in the time series. ... | [
"Explainability",
"Time series data",
"Audio data"
] | https://openreview.net/pdf?id=JBH3mtjG9I | https://openreview.net/forum?id=JBH3mtjG9I | fpYvMYBEDz | decision | 1,730,901,554,627 | JBH3mtjG9I | [
"everyone"
] | [
"NLDL.org/2025/Conference/Program_Chairs"
] | NLDL.org/2025/Conference | 2025 | title: Paper Decision
decision: Accept (Poster)
comment: We recommend a poster presentation given the AC and reviewers recommendations. |
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