Understanding deep learning requires rethinking generalization

10 November 2016

Benjamin Recht

Papers citing "Understanding deep learning requires rethinking generalization"

50 / 1,027 papers shown

Title
Posterior Meta-Replay for Continual Learning Christian Henning Maria R. Cervera Francesco DÁngelo J. Oswald Regina Traber Benjamin Ehret Seijin Kobayashi Benjamin Grewe João Sacramento CLL BDL 51 55 0 01 Mar 2021
DST: Data Selection and joint Training for Learning with Noisy Labels Yi Wei Xue Mei Xin Liu Pengxiang Xu NoLa 27 3 0 01 Mar 2021
Neuron Coverage-Guided Domain Generalization Chris Xing Tian Haoliang Li Xiaofei Xie Yang Liu Shiqi Wang 23 35 0 27 Feb 2021
FINE Samples for Learning with Noisy Labels Taehyeon Kim Jongwoo Ko Sangwook Cho J. Choi Se-Young Yun NoLa 38 103 0 23 Feb 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark W. Schmidt Simon Lacoste-Julien 18 18 0 18 Feb 2021
Dissecting Supervised Contrastive Learning Florian Graf Christoph Hofer Marc Niethammer Roland Kwitt SSL 117 70 0 17 Feb 2021
Evaluating Multi-label Classifiers with Noisy Labels Wenting Zhao Carla P. Gomes NoLa 74 14 0 16 Feb 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model Bruno Loureiro Cédric Gerbelot Hugo Cui Sebastian Goldt Florent Krzakala M. Mézard Lenka Zdeborová 35 136 0 16 Feb 2021
Training Larger Networks for Deep Reinforcement Learning Keita Ota Devesh K. Jha Asako Kanezaki OffRL 25 39 0 16 Feb 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs S. Feizi AAML 37 45 0 15 Feb 2021
Learning by Turning: Neural Architecture Aware Optimisation Yang Liu Jeremy Bernstein M. Meister Yisong Yue ODL 43 26 0 14 Feb 2021
Technical Challenges for Training Fair Neural Networks Valeriia Cherepanova V. Nanda Micah Goldblum John P. Dickerson Tom Goldstein FaML 25 22 0 12 Feb 2021
Understanding the Interaction of Adversarial Training with Noisy Labels Jianing Zhu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Hongxia Yang Mohan Kankanhalli Masashi Sugiyama AAML 27 27 0 06 Feb 2021
On the Reproducibility of Neural Network Predictions Srinadh Bhojanapalli Kimberly Wilber Andreas Veit A. S. Rawat Seungyeon Kim A. Menon Sanjiv Kumar 29 35 0 05 Feb 2021
Provably End-to-end Label-Noise Learning without Anchor Points Xuefeng Li Tongliang Liu Bo Han Gang Niu Masashi Sugiyama NoLa 133 121 0 04 Feb 2021
SGD Generalizes Better Than GD (And Regularization Doesn't Help) I Zaghloul Amir Tomer Koren Roi Livni 29 46 0 01 Feb 2021
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks Asaf Noy Yi Tian Xu Y. Aflalo Lihi Zelnik-Manor Rong Jin 41 3 0 12 Jan 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 82 216 0 11 Jan 2021
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise Spencer Frei Yuan Cao Quanquan Gu FedML MLT 70 19 0 04 Jan 2021
A Second-Order Approach to Learning with Instance-Dependent Label Noise Zhaowei Zhu Tongliang Liu Yang Liu NoLa 22 126 0 22 Dec 2020
Neural Joint Entropy Estimation Yuval Shalev Amichai Painsky I. Ben-Gal 34 8 0 21 Dec 2020
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification A. Aksoy Mahdyar Ravanbakhsh Begüm Demir 35 24 0 19 Dec 2020
Attentional-Biased Stochastic Gradient Descent Q. Qi Yi Tian Xu Rong Jin W. Yin Tianbao Yang ODL 31 12 0 13 Dec 2020
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 40 123 0 10 Dec 2020
A Topological Filter for Learning with Label Noise Pengxiang Wu Songzhu Zheng Mayank Goswami Dimitris N. Metaxas Chao Chen NoLa 30 112 0 09 Dec 2020
Multi-Objective Interpolation Training for Robustness to Label Noise Diego Ortego Eric Arazo Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 30 112 0 08 Dec 2020
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 103 34 0 08 Dec 2020
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and $L_0$ Regularization Yaniv Shulman 46 3 0 07 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 355 0 07 Dec 2020
Cross-Layer Distillation with Semantic Calibration Defang Chen Jian-Ping Mei Yuan Zhang Can Wang Yan Feng Chun-Yen Chen FedML 45 287 0 06 Dec 2020
Every Model Learned by Gradient Descent Is Approximately a Kernel Machine Pedro M. Domingos MLT 29 71 0 30 Nov 2020
Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical Imaging Marija Vella João F. C. Mota 19 4 0 29 Nov 2020
Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint A. Paul P. Mohan Stuti Sehgal 14 17 0 27 Nov 2020
Gradient Starvation: A Learning Proclivity in Neural Networks Mohammad Pezeshki Sekouba Kaba Yoshua Bengio Aaron Courville Doina Precup Guillaume Lajoie MLT 50 258 0 18 Nov 2020
Dynamic Hard Pruning of Neural Networks at the Edge of the Internet Lorenzo Valerio F. M. Nardini A. Passarella R. Perego 25 12 0 17 Nov 2020
Chaos and Complexity from Quantum Neural Network: A study with Diffusion Metric in Machine Learning S. Choudhury Ankan Dutta Debisree Ray 22 21 0 16 Nov 2020
Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting Zeke Xie Fengxiang He Shaopeng Fu Issei Sato Dacheng Tao Masashi Sugiyama 21 60 0 12 Nov 2020
A Survey of Label-noise Representation Learning: Past, Present and Future Bo Han Quanming Yao Tongliang Liu Gang Niu Ivor W. Tsang James T. Kwok Masashi Sugiyama NoLa 24 159 0 09 Nov 2020
Teaching with Commentaries Aniruddh Raghu M. Raghu Simon Kornblith David Duvenaud Geoffrey E. Hinton 27 24 0 05 Nov 2020
Generalized Negative Correlation Learning for Deep Ensembling Sebastian Buschjäger Lukas Pfahler K. Morik FedML BDL UQCV 11 17 0 05 Nov 2020
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate Jingfeng Wu Difan Zou Vladimir Braverman Quanquan Gu 8 18 0 04 Nov 2020
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition Ben Adlam Jeffrey Pennington UD 39 93 0 04 Nov 2020
Latent Causal Invariant Model Xinwei Sun Botong Wu Xiangyu Zheng Chang-Shu Liu Wei Chen Tao Qin Tie-Yan Liu OOD CML BDL 29 14 0 04 Nov 2020
Understanding Capacity-Driven Scale-Out Neural Recommendation Inference Michael Lui Yavuz Yetim Özgür Özkan Zhuoran Zhao Shin-Yeh Tsai Carole-Jean Wu Mark Hempstead GNN BDL LRM 22 51 0 04 Nov 2020
Instance based Generalization in Reinforcement Learning Martín Bertrán Natalia Martínez Mariano Phielipp Guillermo Sapiro OffRL 27 16 0 02 Nov 2020
A Bayesian Perspective on Training Speed and Model Selection Clare Lyle Lisa Schut Binxin Ru Y. Gal Mark van der Wilk 44 24 0 27 Oct 2020
Memorizing without overfitting: Bias, variance, and interpolation in over-parameterized models J. Rocks Pankaj Mehta 23 41 0 26 Oct 2020
Robust and Verifiable Information Embedding Attacks to Deep Neural Networks via Error-Correcting Codes Jinyuan Jia Binghui Wang Neil Zhenqiang Gong AAML 35 5 0 26 Oct 2020
An Investigation of how Label Smoothing Affects Generalization Blair Chen Liu Ziyin Zihao Wang Paul Pu Liang UQCV 21 17 0 23 Oct 2020
Deep Learning is Singular, and That's Good Daniel Murfet Susan Wei Biwei Huang Hui Li Jesse Gell-Redman T. Quella UQCV 24 26 0 22 Oct 2020