Datasets:

J0nasW
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paperswithcode

paperID stringlengths 36 36	pwc_id stringlengths 8 47	arxiv_id stringlengths 6 16 ⌀	nips_id null	url_abs stringlengths 18 329	url_pdf stringlengths 18 742	title stringlengths 8 325	abstract stringlengths 1 7.27k ⌀	authors stringlengths 2 7.06k	published stringlengths 10 10 ⌀	conference nulllengths 12 47 ⌀	conference_url_abs nulllengths 16 198 ⌀	conference_url_pdf nulllengths 27 199 ⌀	proceeding stringlengths 6 47 ⌀	taskID stringlengths 7 1.44k	areaID stringclasses 688 values	embedding stringlengths 9.26k 12.5k	umap_embedding stringlengths 29 44
91de2389-e4fa-47a8-b1d8-e711c5f1c68f	neural-concept-formation-in-knowledge-graphs	null	null	https://openreview.net/forum?id=V61-62OS4mZ	https://openreview.net/pdf?id=V61-62OS4mZ	Neural Concept Formation in Knowledge Graphs	In this work, we investigate how to learn novel concepts in Knowledge Graphs (KGs) in a principled way, and how to effectively exploit them to produce more accurate neural link prediction models. Specifically, we show how concept membership relationships learned via unsupervised clustering of entities can be reified an...	['Pasquale Minervini', 'Antonio Vergari', 'Agnieszka Dobrowolska']	2021-06-22	null	null	null	akbc-2021-10	['novel-concepts']	['reasoning']	[ 1.54296324e-01 8.67769837e-01 -6.80537403e-01 -6.45391524e-01 -2.36760721e-01 -3.32420886e-01 3.21055084e-01 4.21708882e-01 -3.44073683e-01 1.20781672e+00 1.37622237e-01 -2.46026158e-01 -3.62160653e-01 -1.14378023e+00 -1.16703248e+00 -1.55001879e-01 -5.35619795e-01 7.03670561e-01 2.03727037e-01 -1.64004818...	[8.88937759399414, 7.979780673980713]
5657ebcc-0485-4be6-9c31-8ca3fb8c111a	a-large-scale-study-of-language-models-for	1804.01849	null	http://arxiv.org/abs/1804.01849v1	http://arxiv.org/pdf/1804.01849v1.pdf	A Large-Scale Study of Language Models for Chord Prediction	We conduct a large-scale study of language models for chord prediction. Specifically, we compare N-gram models to various flavours of recurrent neural networks on a comprehensive dataset comprising all publicly available datasets of annotated chords known to us. This large amount of data allows us to systematically exp...	['Filip Korzeniowski', 'David R. W. Sears', 'Gerhard Widmer']	2018-04-05	null	null	null	null	['chord-recognition']	['audio']	[ 1.31269753e-01 -1.49378553e-01 -1.12563297e-02 -5.72638437e-02 -6.83169484e-01 -1.00801635e+00 3.60530078e-01 -4.51352932e-02 -5.17974138e-01 4.59509373e-01 5.72915971e-01 -2.67934382e-01 -1.70638099e-01 -6.52473748e-01 -2.88768828e-01 -5.65615356e-01 -3.11165273e-01 6.61789775e-01 5.04404008e-01 -7.02310681...	[15.893318176269531, 5.330941200256348]
6bb64aa4-f278-433d-8d44-b75d3ffadc49	consistent-and-symmetry-preserving-data	2104.11578	null	https://arxiv.org/abs/2104.11578v1	https://arxiv.org/pdf/2104.11578v1.pdf	Consistent and symmetry preserving data-driven interface reconstruction for the level-set method	Recently, machine learning has been used to substitute parts of conventional computational fluid dynamics, e.g. the cell-face reconstruction in finite-volume solvers or the curvature computation in the Volume-of-Fluid (VOF) method. The latter showed improvements in terms of accuracy for coarsely resolved interfaces, ho...	['Nikolaus Adams', 'Deniz A. Bezgin', 'Aaron B. Buhendwa']	2021-04-23	null	null	null	null	['face-reconstruction']	['computer-vision']	[ 1.12194330e-01 -1.17852084e-01 4.91536885e-01 2.18671620e-01 -6.04186475e-01 -1.84181243e-01 6.49611294e-01 3.24491858e-01 -3.71626735e-01 9.88221288e-01 -2.43738443e-01 -3.24923217e-01 -3.40482146e-01 -9.80118155e-01 -4.43558455e-01 -1.04268193e+00 1.76164676e-02 7.28078723e-01 1.59679070e-01 -2.62490511...	[6.387056827545166, 3.3242926597595215]
3963eb53-5252-41fe-a220-3e7e72c7c72f	resources-and-evaluations-for-multi	2306.12601	null	https://arxiv.org/abs/2306.12601v1	https://arxiv.org/pdf/2306.12601v1.pdf	Resources and Evaluations for Multi-Distribution Dense Information Retrieval	We introduce and define the novel problem of multi-distribution information retrieval (IR) where given a query, systems need to retrieve passages from within multiple collections, each drawn from a different distribution. Some of these collections and distributions might not be available at training time. To evaluate m...	['Simran Arora', 'Omar Khattab', 'Soumya Chatterjee']	2023-06-21	null	null	null	null	['retrieval', 'question-answering', 'information-retrieval']	['methodology', 'natural-language-processing', 'natural-language-processing']	[-2.10464269e-01 -5.10327697e-01 -5.26404142e-01 -2.33647972e-01 -1.98825192e+00 -1.10904813e+00 6.78387940e-01 5.10692894e-01 -5.34735560e-01 9.06216025e-01 1.99299380e-01 -8.69033709e-02 -6.29670799e-01 -7.66481400e-01 -6.29947305e-01 -3.76926154e-01 1.20171323e-01 1.33626032e+00 6.35550082e-01 -3.41625720...	[11.454911231994629, 7.704309463500977]
b328b38b-0cbc-44e0-b008-7896e324eaa0	chili-pepper-disease-diagnosis-via-image	2306.12057	null	https://arxiv.org/abs/2306.12057v1	https://arxiv.org/pdf/2306.12057v1.pdf	Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder	With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be ...	['Sungyoung Kim', 'Jongwook Si']	2023-06-21	null	null	null	null	['image-reconstruction', 'anomaly-detection']	['computer-vision', 'methodology']	[ 3.03958982e-01 -3.16793501e-01 1.62073508e-01 -1.66590855e-01 -6.73410371e-02 -2.61246473e-01 1.68067962e-01 -1.79791704e-01 2.76662922e-03 4.86118883e-01 -4.02705550e-01 6.00404851e-02 -1.01132356e-01 -1.41629064e+00 -4.16858345e-01 -1.01171851e+00 3.96085948e-01 1.16422191e-01 6.36012927e-02 -2.81823158...	[7.676959991455078, 1.907288908958435]
c13f7897-3384-4256-85c6-222f39ed7c89	channel-recurrent-attention-networks-for	2010.03108	null	https://arxiv.org/abs/2010.03108v1	https://arxiv.org/pdf/2010.03108v1.pdf	Channel Recurrent Attention Networks for Video Pedestrian Retrieval	Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we propose a fully attentional network, termed {\it channel recurrent attention network}, for the task of video pedestrian retrieval. The main atte...	['Mehrtash Harandi', 'Lars Petersson', 'Jieming Zhou', 'Pan Ji', 'Pengfei Fang']	2020-10-07	null	null	null	null	['person-retrieval']	['computer-vision']	[ 3.31758559e-01 -4.68979299e-01 -1.05405629e-01 -6.46921322e-02 -6.49204075e-01 -2.54998449e-02 6.67910695e-01 -3.15437496e-01 -3.57983410e-01 5.90136170e-01 6.06874585e-01 1.86678290e-01 1.51827484e-01 -5.35718501e-01 -8.61878991e-01 -7.90900230e-01 -8.10200050e-02 -2.36137047e-01 9.76259857e-02 5.31656183...	[9.441946983337402, 0.6835160255432129]
07343c04-d472-4117-93fd-aaedd6793ec2	can-neural-networks-do-arithmetic-a-survey-on	2303.07735	null	https://arxiv.org/abs/2303.07735v1	https://arxiv.org/pdf/2303.07735v1.pdf	Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models	Creating learning models that can exhibit sophisticated reasoning skills is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network arc...	['Alberto Testolin']	2023-03-14	null	null	null	null	['numerical-integration', 'automated-theorem-proving', 'automated-theorem-proving']	['miscellaneous', 'miscellaneous', 'reasoning']	[-1.94684893e-01 -1.03545956e-01 -2.23597452e-01 -2.23596275e-01 -2.01922163e-01 -6.41880989e-01 7.34340131e-01 5.51803887e-01 -4.35138553e-01 8.27689946e-01 -2.87558585e-01 -9.73234534e-01 -4.46500242e-01 -1.21110022e+00 -7.15499222e-01 -2.55844891e-01 -3.75246882e-01 5.26835799e-01 -2.19003975e-01 -5.23171842...	[9.254510879516602, 7.161590099334717]
372e1a7f-7a3a-4cdf-af48-6fd0413ca8d8	pac-assisted-value-factorisation-with	2206.11420	null	https://arxiv.org/abs/2206.11420v3	https://arxiv.org/pdf/2206.11420v3.pdf	PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning	Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods. It allows optimizing a joint action-value function through the maximization of factorized per-agent utilities due to monotonicity. In this paper, we show that in partially observabl...	['Vaneet Aggarwal', 'Tian Lan', 'Hanhan Zhou']	2022-06-22	null	null	null	null	['starcraft-ii']	['playing-games']	[ 9.33378178e-04 3.37637067e-01 -7.03318775e-01 -7.28595704e-02 -1.04379749e+00 -4.86216396e-01 7.58728504e-01 1.25225976e-01 -8.62132728e-01 1.56500614e+00 4.47000980e-01 -1.93858057e-01 -4.48424280e-01 -7.45422244e-01 -8.85571718e-01 -9.10247803e-01 -6.83379650e-01 7.03703523e-01 -2.49959201e-01 -3.14699680...	[3.7670390605926514, 2.0681231021881104]
86976613-7bf1-456b-82c5-d500533d2921	monocular-3d-object-detection-using-multi	2212.11804	null	https://arxiv.org/abs/2212.11804v1	https://arxiv.org/pdf/2212.11804v1.pdf	Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference	3D object detection is vital as it would enable us to capture objects' sizes, orientation, and position in the world. As a result, we would be able to use this 3D detection in real-world applications such as Augmented Reality (AR), self-driving cars, and robotics which perceive the world the same way we do as humans. M...	['Ashish Patel', 'Abonia Sojasingarayar']	2022-12-22	null	null	null	null	['monocular-3d-object-detection']	['computer-vision']	[ 9.89828184e-02 -1.63990825e-01 2.02761710e-01 -2.35028028e-01 9.24237967e-02 -7.84826398e-01 4.96305585e-01 -2.50436049e-02 -4.58533257e-01 2.91924417e-01 -5.47745168e-01 -4.77302819e-01 4.83384699e-01 -6.86647594e-01 -6.26309335e-01 -3.28630507e-01 1.65992066e-01 5.87148786e-01 9.19364214e-01 -2.90166825...	[7.7036943435668945, -2.5074880123138428]
252dfae8-3c79-4859-8731-65362d70fa17	towards-a-better-understanding-of	2305.18491	null	https://arxiv.org/abs/2305.18491v1	https://arxiv.org/pdf/2305.18491v1.pdf	Towards a Better Understanding of Representation Dynamics under TD-learning	TD-learning is a foundation reinforcement learning (RL) algorithm for value prediction. Critical to the accuracy of value predictions is the quality of state representations. In this work, we consider the question: how does end-to-end TD-learning impact the representation over time? Complementary to prior work, we prov...	['Rémi Munos', 'Yunhao Tang']	2023-05-29	null	null	null	null	['value-prediction']	['computer-code']	[ 3.80673148e-02 2.57456988e-01 -7.59773910e-01 -1.18984714e-01 -8.37758243e-01 -7.46941507e-01 5.37485898e-01 3.18429887e-01 -4.77282286e-01 1.03467464e+00 3.78732532e-01 -5.25980055e-01 -5.67838490e-01 -5.89721203e-01 -7.18707085e-01 -5.01070082e-01 -6.04621530e-01 4.44654077e-01 -6.87963970e-04 -5.53710878...	[4.067844867706299, 1.9328854084014893]
a4d27304-421c-46e7-8f4e-b6cd0beaa69a	multi-modal-page-stream-segmentation-with	null	null	https://link.springer.com/article/10.1007/s10579-019-09476-2	https://www.inf.uni-hamburg.de/en/inst/ab/lt/publications/2019-wiedemann-lre-pss.pdf	Multi-modal Page Stream Segmentation with Convolutional Neural Networks	In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications. As first steps, the workflow usually involves batch scanning and optical character recognition (OCR) of documents. In the case of multi-page ...	['Gerhard Heyer', 'Gregor Wiedemann']	2019-09-27	null	null	null	lang-resources-evaluation-2019-9	['page-stream-segmentation']	['natural-language-processing']	[ 6.70916975e-01 -1.95391372e-01 1.54130861e-01 -2.95114279e-01 -1.16281581e+00 -8.68752360e-01 5.69551170e-01 4.09916759e-01 -4.00897682e-01 3.82297307e-01 -6.19797818e-02 -3.81673992e-01 -2.39493340e-01 -6.03458226e-01 -6.65960968e-01 -2.46021464e-01 3.54760557e-01 8.67896736e-01 3.68163407e-01 1.07595190...	[11.750812530517578, 2.6896584033966064]
31bf57ad-19aa-4903-96d6-71fe643559c7	video-face-clustering-with-unknown-number-of	1908.03381	null	https://arxiv.org/abs/1908.03381v2	https://arxiv.org/pdf/1908.03381v2.pdf	Video Face Clustering with Unknown Number of Clusters	Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing. We address the challenging problem of clustering face tracks based on their identity. Different from previous work in this area, we choose to operate in a realistic and difficult setting where: (i) the n...	['Sanja Fidler', 'Marc T. Law', 'Makarand Tapaswi']	2019-08-09	null	null	null	iccv-2019-10	['face-clustering']	['computer-vision']	[ 6.90754205e-02 -6.53285980e-02 -2.68651247e-01 -3.28140646e-01 -5.03036916e-01 -8.36068809e-01 5.81524193e-01 2.08750412e-01 -4.23652768e-01 3.41593415e-01 -1.22059703e-01 -7.69765377e-02 -1.24244347e-01 -5.36969364e-01 -5.45166552e-01 -7.11296618e-01 -1.84486344e-01 8.21935058e-01 1.97814584e-01 1.63019478...	[13.462641716003418, 1.0532432794570923]
1fd2aa26-8c5c-4b6d-bea3-fb247190d80c	multi-view-subspace-clustering-via-partition	1912.01201	null	https://arxiv.org/abs/1912.01201v1	https://arxiv.org/pdf/1912.01201v1.pdf	Multi-view Subspace Clustering via Partition Fusion	Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Basically, it integrates multi-view information into graphs, which are then fed into sp...	['Zenglin Xu', 'Boyu Wang', 'Zhao Kang', 'Juncheng Lv', 'Luping Ji']	2019-12-03	null	null	null	null	['multi-view-subspace-clustering']	['computer-vision']	[-1.57365635e-01 -5.27896643e-01 -1.45767763e-01 -5.70858158e-02 -5.12539804e-01 -7.55040526e-01 3.75528932e-01 7.99397826e-02 1.44503817e-01 2.03171283e-01 4.52410221e-01 1.74532682e-01 -4.09128547e-01 -7.21879840e-01 -1.36038601e-01 -9.43844259e-01 2.30492398e-01 2.18531981e-01 3.64398628e-01 1.33844435...	[8.217903137207031, 4.655970096588135]

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A cleaned dataset from paperswithcode.com

Last dataset update: July 2023

This is a cleaned up dataset optained from paperswithcode.com through their API service. It represents a set of around 56K carefully categorized papers into 3K tasks and 16 areas. The papers contain arXiv and NIPS IDs as well as title, abstract and other meta information. It can be used for training text classifiers that concentrate on the use of specific AI and ML methods and frameworks.

It contains the following tables:

papers.csv (around 56K)
papers_train.csv (80% from 56K)
papers_test.csv (20% from 56K)
tasks.csv
areas.csv

Specials

UUIDs were added to the dataset since the PapersWithCode IDs (pwc_ids) are not distinct enough. These UUIDs may change in the future with new versions of the dataset. Also, embeddings were calculated for all of the 56K papers using the brilliant model SciNCL as well as dimensionality-redused 2D coordinates using UMAP.

There is also a simple Python Notebook which was used to optain and refactor the dataset.

Downloads last month: 85

A cleaned dataset from paperswithcode.com

Contents

Specials