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Gradient Descent Finds Global Minima of Deep Neural Networks

9 November 2018

Papers citing "Gradient Descent Finds Global Minima of Deep Neural Networks"

50 / 466 papers shown

Title
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions Ameya Dilip Jagtap Yeonjong Shin Kenji Kawaguchi George Karniadakis ODL 111 137 0 20 May 2021
The Dynamics of Gradient Descent for Overparametrized Neural Networks Siddhartha Satpathi R. Srikant MLT AI4CE 60 14 0 13 May 2021
Why Does Multi-Epoch Training Help? Yi Tian Xu Qi Qian Hao Li Rong Jin 62 1 0 13 May 2021
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis Baihe Huang Xiaoxiao Li Zhao Song Xin Yang FedML 72 16 0 11 May 2021
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth Keyulu Xu Mozhi Zhang Stefanie Jegelka Kenji Kawaguchi GNN 53 78 0 10 May 2021
A Geometric Analysis of Neural Collapse with Unconstrained Features Zhihui Zhu Tianyu Ding Jinxin Zhou Xiao Li Chong You Jeremias Sulam Qing Qu 88 204 0 06 May 2021
RATT: Leveraging Unlabeled Data to Guarantee Generalization Saurabh Garg Sivaraman Balakrishnan J. Zico Kolter Zachary Chase Lipton 82 30 0 01 May 2021
Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors Yuling Jiao Guohao Shen Yuanyuan Lin Jian Huang 121 52 0 14 Apr 2021
A Theoretical Analysis of Learning with Noisily Labeled Data Yi Tian Xu Qi Qian Hao Li Rong Jin NoLa 31 1 0 08 Apr 2021
Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks Yuval Belfer Amnon Geifman Meirav Galun Ronen Basri 74 17 0 07 Apr 2021
Learning with Neural Tangent Kernels in Near Input Sparsity Time A. Zandieh 64 0 0 01 Apr 2021
A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions Arnulf Jentzen Adrian Riekert MLT 81 13 0 01 Apr 2021
Model Order Reduction based on Runge-Kutta Neural Network Qinyu Zhuang Juan M Lorenzi H. Bungartz D. Hartmann 41 15 0 25 Mar 2021
The Low-Rank Simplicity Bias in Deep Networks Minyoung Huh H. Mobahi Richard Y. Zhang Brian Cheung Pulkit Agrawal Phillip Isola 117 116 0 18 Mar 2021
Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks Thanh Nguyen-Tang Sunil R. Gupta Hung The Tran Svetha Venkatesh OffRL 137 7 0 11 Mar 2021
Asymptotics of Ridge Regression in Convolutional Models Mojtaba Sahraee-Ardakan Tung Mai Anup B. Rao Ryan Rossi S. Rangan A. Fletcher MLT 42 2 0 08 Mar 2021
Shift Invariance Can Reduce Adversarial Robustness Songwei Ge Vasu Singla Ronen Basri David Jacobs AAML OOD 85 28 0 03 Mar 2021
Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective Wei Huang Yayong Li Weitao Du Jie Yin R. Xu Ling-Hao Chen Miao Zhang 73 17 0 03 Mar 2021
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels Eran Malach Pritish Kamath Emmanuel Abbe Nathan Srebro 88 39 0 01 Mar 2021
Experiments with Rich Regime Training for Deep Learning Xinyan Li A. Banerjee 73 2 0 26 Feb 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 107 91 0 25 Feb 2021
Convergence rates for gradient descent in the training of overparameterized artificial neural networks with biases Arnulf Jentzen T. Kröger ODL 73 7 0 23 Feb 2021
A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions Patrick Cheridito Arnulf Jentzen Adrian Riekert Florian Rossmannek 69 25 0 19 Feb 2021
A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space Kuo Gai Shihua Zhang 96 8 0 18 Feb 2021
Understanding self-supervised Learning Dynamics without Contrastive Pairs Yuandong Tian Xinlei Chen Surya Ganguli SSL 254 286 0 12 Feb 2021
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network Mo Zhou Rong Ge Chi Jin 145 46 0 04 Feb 2021
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training Cong Fang Hangfeng He Qi Long Weijie J. Su FAtt 203 172 0 29 Jan 2021
Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration Song Mei Theodor Misiakiewicz Andrea Montanari 105 113 0 26 Jan 2021
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths Quynh N. Nguyen 126 49 0 24 Jan 2021
Non-Convex Compressed Sensing with Training Data G. Welper 65 1 0 20 Jan 2021
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks Asaf Noy Yi Tian Xu Y. Aflalo Lihi Zelnik-Manor Rong Jin 71 3 0 12 Jan 2021
BN-invariant sharpness regularizes the training model to better generalization Mingyang Yi Huishuai Zhang Wei Chen Zhi-Ming Ma Tie-Yan Liu 128 3 0 08 Jan 2021
Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis Atsushi Nitanda Denny Wu Taiji Suzuki 86 29 0 31 Dec 2020
Reservoir Transformers Sheng Shen Alexei Baevski Ari S. Morcos Kurt Keutzer Michael Auli Douwe Kiela 88 18 0 30 Dec 2020
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks Quynh N. Nguyen Marco Mondelli Guido Montúfar 87 83 0 21 Dec 2020
Recent advances in deep learning theory Fengxiang He Dacheng Tao AI4CE 130 51 0 20 Dec 2020
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning Zeyuan Allen-Zhu Yuanzhi Li FedML 187 376 0 17 Dec 2020
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks Xiangyu Chang Yingcong Li Samet Oymak Christos Thrampoulidis 86 51 0 16 Dec 2020
On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers E. Weinan Stephan Wojtowytsch 90 45 0 10 Dec 2020
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods Taiji Suzuki Shunta Akiyama MLT 65 12 0 06 Dec 2020
Effect of the initial configuration of weights on the training and function of artificial neural networks Ricardo J. Jesus Mário Antunes R. A. D. Costa S. Dorogovtsev J. F. F. Mendes R. Aguiar 69 15 0 04 Dec 2020
Neural Contextual Bandits with Deep Representation and Shallow Exploration Pan Xu Zheng Wen Handong Zhao Quanquan Gu OffRL 89 78 0 03 Dec 2020
Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling model Michael Kohler S. Langer 74 19 0 27 Nov 2020
Tight Hardness Results for Training Depth-2 ReLU Networks Surbhi Goel Adam R. Klivans Pasin Manurangsi Daniel Reichman 78 41 0 27 Nov 2020
Implicit bias of deep linear networks in the large learning rate phase Wei Huang Weitao Du R. Xu Chunrui Liu 77 2 0 25 Nov 2020
Normalization effects on shallow neural networks and related asymptotic expansions Jiahui Yu K. Spiliopoulos 53 6 0 20 Nov 2020
Coresets for Robust Training of Neural Networks against Noisy Labels Baharan Mirzasoleiman Kaidi Cao J. Leskovec NoLa 79 32 0 15 Nov 2020
Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study Cheng Chen Junjie Yang Yi Zhou 45 0 0 13 Nov 2020
Towards NNGP-guided Neural Architecture Search Daniel S. Park Jaehoon Lee Daiyi Peng Yuan Cao Jascha Narain Sohl-Dickstein BDL 71 34 0 11 Nov 2020
Towards a Better Global Loss Landscape of GANs Ruoyu Sun Tiantian Fang Alex Schwing GAN 67 26 0 10 Nov 2020