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Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian

12 June 2019

Papers citing "Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian"

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Title
LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation Can Jin Ying Li Mingyu Zhao Shiyu Zhao Zhenting Wang Xiaoxiao He Ligong Han Tong Che Dimitris N. Metaxas VPVLM VLM 303 2 0 02 Feb 2025
Parameter-Efficient Fine-Tuning for Foundation Models Dan Zhang Tao Feng Lilong Xue Yuandong Wang Yuxiao Dong J. Tang 220 12 0 23 Jan 2025
Length independent generalization bounds for deep SSM architectures via Rademacher contraction and stability constraints Dániel Rácz Mihaly Petreczky Bálint Daróczy 125 1 0 30 May 2024
Understanding Generalization through Visualizations Wenjie Huang Z. Emam Micah Goldblum Liam H. Fowl J. K. Terry Furong Huang Tom Goldstein AI4CE 51 80 0 07 Jun 2019
Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience Vaishnavh Nagarajan J. Zico Kolter 93 101 0 30 May 2019
Generalization bounds for deep convolutional neural networks Philip M. Long Hanie Sedghi MLT 109 90 0 29 May 2019
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems Atsushi Nitanda Geoffrey Chinot Taiji Suzuki MLT 76 34 0 23 May 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 83 243 0 27 Apr 2019
A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics E. Weinan Chao Ma Lei Wu MLT 64 123 0 08 Apr 2019
On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai Ohad Shamir MLT 118 182 0 01 Apr 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 122 353 0 27 Mar 2019
Two models of double descent for weak features M. Belkin Daniel J. Hsu Ji Xu 117 375 0 18 Mar 2019
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks Samet Oymak Mahdi Soltanolkotabi 61 322 0 12 Feb 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 221 974 0 24 Jan 2019
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits Xialiang Dou Tengyuan Liang MLT 81 42 0 21 Jan 2019
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? Samet Oymak Mahdi Soltanolkotabi ODL 55 177 0 25 Dec 2018
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 111 840 0 19 Dec 2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 204 448 0 21 Nov 2018
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 201 775 0 12 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 275 1,469 0 09 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du Jason D. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 229 1,136 0 09 Nov 2018
Rademacher Complexity for Adversarially Robust Generalization Dong Yin Kannan Ramchandran Peter L. Bartlett AAML 99 261 0 29 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 241 1,276 0 04 Oct 2018
Gradient descent aligns the layers of deep linear networks Ziwei Ji Matus Telgarsky 123 257 0 04 Oct 2018
Mean Field Analysis of Neural Networks: A Central Limit Theorem Justin A. Sirignano K. Spiliopoulos MLT 89 194 0 28 Aug 2018
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize Tengyuan Liang Alexander Rakhlin 89 355 0 01 Aug 2018
Does data interpolation contradict statistical optimality? M. Belkin Alexander Rakhlin Alexandre B. Tsybakov 93 221 0 25 Jun 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 275 3,223 0 20 Jun 2018
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate M. Belkin Daniel J. Hsu P. Mitra AI4CE 153 259 0 13 Jun 2018
Implicit Bias of Gradient Descent on Linear Convolutional Networks Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro MDE 130 414 0 01 Jun 2018
On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport Lénaïc Chizat Francis R. Bach OT 221 737 0 24 May 2018
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 107 863 0 18 Apr 2018
Risk and parameter convergence of logistic regression Ziwei Ji Matus Telgarsky 73 130 0 20 Mar 2018
Stronger generalization bounds for deep nets via a compression approach Sanjeev Arora Rong Ge Behnam Neyshabur Yi Zhang MLT AI4CE 93 643 0 14 Feb 2018
Learning One Convolutional Layer with Overlapping Patches Surbhi Goel Adam R. Klivans Raghu Meka MLT 80 81 0 07 Feb 2018
Visualizing the Loss Landscape of Neural Nets Hao Li Zheng Xu Gavin Taylor Christoph Studer Tom Goldstein 266 1,901 0 28 Dec 2017
Size-Independent Sample Complexity of Neural Networks Noah Golowich Alexander Rakhlin Ohad Shamir 157 551 0 18 Dec 2017
The Implicit Bias of Gradient Descent on Separable Data Daniel Soudry Elad Hoffer Mor Shpigel Nacson Suriya Gunasekar Nathan Srebro 172 924 0 27 Oct 2017
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data Alon Brutzkus Amir Globerson Eran Malach Shai Shalev-Shwartz MLT 156 279 0 27 Oct 2017
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks Behnam Neyshabur Srinadh Bhojanapalli Nathan Srebro 90 610 0 29 Jul 2017
Exploring Generalization in Deep Learning Behnam Neyshabur Srinadh Bhojanapalli David A. McAllester Nathan Srebro FAtt 162 1,259 0 27 Jun 2017
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks Levent Sagun Utku Evci V. U. Güney Yann N. Dauphin Léon Bottou 58 419 0 14 Jun 2017
Train longer, generalize better: closing the generalization gap in large batch training of neural networks Elad Hoffer Itay Hubara Daniel Soudry ODL 185 800 0 24 May 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite Daniel M. Roy 117 820 0 31 Mar 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 351 4,636 0 10 Nov 2016
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys Pratik Chaudhari A. Choromańska Stefano Soatto Yann LeCun Carlo Baldassi C. Borgs J. Chayes Levent Sagun R. Zecchina ODL 96 775 0 06 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 436 2,946 0 15 Sep 2016
A vector-contraction inequality for Rademacher complexities Andreas Maurer 87 261 0 01 May 2016
Train faster, generalize better: Stability of stochastic gradient descent Moritz Hardt Benjamin Recht Y. Singer 118 1,243 0 03 Sep 2015
A useful variant of the Davis--Kahan theorem for statisticians Yi Yu Tengyao Wang R. Samworth 105 578 0 04 May 2014