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05/25/2023
Sharpness-Aware Minimization Leads to Low-Rank Features
Sharpness-aware minimization (SAM) is a recently proposed method that mi...
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01/30/2023
On student-teacher deviations in distillation: does it pay to disobey?
Knowledge distillation has been widely-used to improve the performance o...
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10/16/2021
Sharpness-Aware Minimization Improves Language Model Generalization
The allure of superhuman-level capabilities has led to considerable inte...
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03/18/2021
The Low-Rank Simplicity Bias in Deep Networks
Modern deep neural networks are highly over-parameterized compared to th...
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12/14/2020
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning
Understanding generalization in deep learning is arguably one of the mos...
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11/05/2020
Data Augmentation via Structured Adversarial Perturbations
Data augmentation is a major component of many machine learning methods ...
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10/06/2020
A Unifying View on Implicit Bias in Training Linear Neural Networks
We study the implicit bias of gradient flow (i.e., gradient descent with...
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10/03/2020
Sharpness-Aware Minimization for Efficiently Improving Generalization
In today's heavily overparameterized models, the value of the training l...
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02/13/2020
Self-Distillation Amplifies Regularization in Hilbert Space
Knowledge distillation introduced in the deep learning context is a meth...
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12/04/2019
Fantastic Generalization Measures and Where to Find Them
Generalization of deep networks has been of great interest in recent yea...
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06/10/2019
A Closed-Form Learned Pooling for Deep Classification Networks
In modern computer vision tasks, convolutional neural networks (CNNs) ar...
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01/29/2019
Semantic Redundancies in Image-Classification Datasets: The 10 Don't Need
Large datasets have been crucial to the success of deep learning models ...
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09/28/2018
Predicting the Generalization Gap in Deep Networks with Margin Distributions
As shown in recent research, deep neural networks can perfectly fit rand...
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03/15/2018
Large Margin Deep Networks for Classification
We present a formulation of deep learning that aims at producing a large...
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10/28/2016
Homotopy Analysis for Tensor PCA
Developing efficient and guaranteed nonconvex algorithms has been an imp...
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01/19/2016
A Theory of Local Matching: SIFT and Beyond
Why has SIFT been so successful? Why its extension, DSP-SIFT, can furthe...
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06/17/2015
Learning with a Wasserstein Loss
Learning to predict multi-label outputs is challenging, but in many prob...
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06/18/2010