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UNDO: Understanding Distillation as Optimization
Paper • 2504.02521 • Published -
One Model to Train them All: Hierarchical Self-Distillation for Enhanced Early Layer Embeddings
Paper • 2503.03008 • Published • 1 -
Understanding Self-Distillation in the Presence of Label Noise
Paper • 2301.13304 • Published -
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks
Paper • 2407.03475 • Published
Keira Chen
KeiraYC
AI & ML interests
None yet
Organizations
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TDL project
adversarial attack
-
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models
Paper • 2304.09875 • Published -
Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study
Paper • 2504.02733 • Published -
RADAR: Benchmarking Language Models on Imperfect Tabular Data
Paper • 2506.08249 • Published • 2 -
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
Paper • 2306.04064 • Published
Self-distillation
-
UNDO: Understanding Distillation as Optimization
Paper • 2504.02521 • Published -
One Model to Train them All: Hierarchical Self-Distillation for Enhanced Early Layer Embeddings
Paper • 2503.03008 • Published • 1 -
Understanding Self-Distillation in the Presence of Label Noise
Paper • 2301.13304 • Published -
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks
Paper • 2407.03475 • Published
TDL project
adversarial attack
-
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models
Paper • 2304.09875 • Published -
Enhancing LLM Robustness to Perturbed Instructions: An Empirical Study
Paper • 2504.02733 • Published -
RADAR: Benchmarking Language Models on Imperfect Tabular Data
Paper • 2506.08249 • Published • 2 -
Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
Paper • 2306.04064 • Published
models 0
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datasets 0
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