Why Robust Generalization in Deep Learning is Difficult: Perspective of
Expressive Power

Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power

27 May 2022

Papers citing "Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power"

19 / 19 papers shown

Title
Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Ability Lijia Yu Yibo Miao Yifan Zhu Xiao-Shan Gao Lijun Zhang 48 0 0 06 Mar 2025
Curse of Dimensionality in Neural Network Optimization Sanghoon Na Haizhao Yang 46 0 0 07 Feb 2025
To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement Learning Hilmy Baja Michiel Kallenberg Ioannis Athanasiadis OffRL 46 0 0 22 Jan 2025
Generalizability of Memorization Neural Networks Lijia Yu Xiao-Shan Gao Lijun Zhang Yibo Miao 28 1 0 01 Nov 2024
Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data Binghui Li Yuanzhi Li OOD 28 2 0 11 Oct 2024
Life, uh, Finds a Way: Systematic Neural Search Alex Baranski Jun Tani 18 0 0 02 Oct 2024
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis Zhang Chen Luca Demetrio Srishti Gupta Xiaoyi Feng Zhaoqiang Xia ... Maura Pintor Luca Oneto Ambra Demontis Battista Biggio Fabio Roli AAML 26 1 0 14 Jun 2024
From Robustness to Improved Generalization and Calibration in Pre-trained Language Models Josip Jukić Jan Snajder 26 0 0 31 Mar 2024
Towards White Box Deep Learning Maciej Satkiewicz AAML 27 1 0 14 Mar 2024
Deep Networks Always Grok and Here is Why Ahmed Imtiaz Humayun Randall Balestriero Richard Baraniuk AAML OOD AI4CE 43 19 0 23 Feb 2024
Is Adversarial Training with Compressed Datasets Effective? Tong Chen Raghavendra Selvan AAML 48 0 0 08 Feb 2024
Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint Zhongjie Shi Fanghui Liu Yuan Cao Johan A. K. Suykens 30 0 0 24 Jan 2024
Data-Dependent Stability Analysis of Adversarial Training Yihan Wang Shuang Liu Xiao-Shan Gao 30 3 0 06 Jan 2024
Towards Understanding Clean Generalization and Robust Overfitting in Adversarial Training Binghui Li Yuanzhi Li AAML 24 3 0 02 Jun 2023
On the Importance of Backbone to the Adversarial Robustness of Object Detectors Xiao-Li Li Hang Chen Xiaolin Hu AAML 34 4 0 27 May 2023
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness Peiyu Xiong Michael W. Tegegn Jaskeerat Singh Sarin Shubhraneel Pal Julia Rubin SILM AAML 32 8 0 17 Mar 2023
Understanding CNN Fragility When Learning With Imbalanced Data Damien Dablain Kristen N. Jacobson C. Bellinger Mark Roberts Nitesh V. Chawla 11 39 0 17 Oct 2022
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 90 115 0 28 Feb 2021
Benefits of depth in neural networks Matus Telgarsky 123 602 0 14 Feb 2016