Gradient Descent Provably Optimizes Over-parameterized Neural Networks

4 October 2018

Aarti Singh

Papers citing "Gradient Descent Provably Optimizes Over-parameterized Neural Networks"

50 / 302 papers shown

Title
A Theoretical Analysis of Fine-tuning with Linear Teachers Gal Shachaf Alon Brutzkus Amir Globerson 34 17 0 04 Jul 2021
Random Neural Networks in the Infinite Width Limit as Gaussian Processes Boris Hanin BDL 32 43 0 04 Jul 2021
AutoFormer: Searching Transformers for Visual Recognition Minghao Chen Houwen Peng Jianlong Fu Haibin Ling ViT 36 259 0 01 Jul 2021
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios Alessandro Favero Francesco Cagnetta M. Wyart 30 31 0 16 Jun 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms A. Camuto George Deligiannidis Murat A. Erdogdu Mert Gurbuzbalaban Umut cSimcsekli Lingjiong Zhu 33 29 0 09 Jun 2021
TENGraD: Time-Efficient Natural Gradient Descent with Exact Fisher-Block Inversion Saeed Soori Bugra Can Baourun Mu Mert Gurbuzbalaban M. Dehnavi 24 10 0 07 Jun 2021
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training Yatong Bai Tanmay Gautam Yujie Gai Somayeh Sojoudi AAML 27 3 0 25 May 2021
Global Convergence of Three-layer Neural Networks in the Mean Field Regime H. Pham Phan-Minh Nguyen MLT AI4CE 41 19 0 11 May 2021
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis Baihe Huang Xiaoxiao Li Zhao Song Xin Yang FedML 31 16 0 11 May 2021
RATT: Leveraging Unlabeled Data to Guarantee Generalization Saurabh Garg Sivaraman Balakrishnan J. Zico Kolter Zachary Chase Lipton 30 30 0 01 May 2021
Generalization Guarantees for Neural Architecture Search with Train-Validation Split Samet Oymak Mingchen Li Mahdi Soltanolkotabi AI4CE OOD 36 13 0 29 Apr 2021
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols Aaron Courville Yanpeng Zhao Kewei Tu 23 22 0 28 Apr 2021
Understanding Overparameterization in Generative Adversarial Networks Yogesh Balaji M. Sajedi Neha Kalibhat Mucong Ding Dominik Stöger Mahdi Soltanolkotabi S. Feizi AI4CE 22 21 0 12 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 34 13 0 01 Apr 2021
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation Q. Du Yiqi Gu Haizhao Yang Chao Zhou 26 20 0 21 Mar 2021
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy Lucas Liebenwein Cenk Baykal Brandon Carter David K Gifford Daniela Rus AAML 40 71 0 04 Mar 2021
Experiments with Rich Regime Training for Deep Learning Xinyan Li A. Banerjee 32 2 0 26 Feb 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 46 89 0 25 Feb 2021
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs) Zhiyuan Li Sadhika Malladi Sanjeev Arora 44 78 0 24 Feb 2021
Convergence rates for gradient descent in the training of overparameterized artificial neural networks with biases Arnulf Jentzen T. Kröger ODL 28 7 0 23 Feb 2021
GIST: Distributed Training for Large-Scale Graph Convolutional Networks Cameron R. Wolfe Jingkang Yang Arindam Chowdhury Chen Dun Artun Bayer Santiago Segarra Anastasios Kyrillidis BDL GNN LRM 54 9 0 20 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 28 24 0 19 Feb 2021
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent Shahar Azulay E. Moroshko Mor Shpigel Nacson Blake E. Woodworth Nathan Srebro Amir Globerson Daniel Soudry AI4CE 33 73 0 19 Feb 2021
A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network Mo Zhou Rong Ge Chi Jin 74 45 0 04 Feb 2021
On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths Quynh N. Nguyen 43 48 0 24 Jan 2021
Reproducing Activation Function for Deep Learning Senwei Liang Liyao Lyu Chunmei Wang Haizhao Yang 36 21 0 13 Jan 2021
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks Asaf Noy Yi Tian Xu Y. Aflalo Lihi Zelnik-Manor R. L. Jin 39 3 0 12 Jan 2021
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks Quynh N. Nguyen Marco Mondelli Guido Montúfar 25 81 0 21 Dec 2020
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning Zeyuan Allen-Zhu Yuanzhi Li FedML 60 355 0 17 Dec 2020
On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers E. Weinan Stephan Wojtowytsch 36 43 0 10 Dec 2020
Neural collapse with unconstrained features D. Mixon Hans Parshall Jianzong Pi 28 114 0 23 Nov 2020
Gradient Starvation: A Learning Proclivity in Neural Networks Mohammad Pezeshki Sekouba Kaba Yoshua Bengio Aaron Courville Doina Precup Guillaume Lajoie MLT 50 257 0 18 Nov 2020
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee Tengyu Xu Yingbin Liang Guanghui Lan 42 121 0 11 Nov 2020
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces Zhuoran Yang Chi Jin Zhaoran Wang Mengdi Wang Michael I. Jordan 39 18 0 09 Nov 2020
Federated Knowledge Distillation Hyowoon Seo Jihong Park Seungeun Oh M. Bennis Seong-Lyun Kim FedML 31 91 0 04 Nov 2020
Are wider nets better given the same number of parameters? A. Golubeva Behnam Neyshabur Guy Gur-Ari 27 44 0 27 Oct 2020
Neural Network Approximation: Three Hidden Layers Are Enough Zuowei Shen Haizhao Yang Shijun Zhang 30 115 0 25 Oct 2020
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks Zhiqi Bu Shiyun Xu Kan Chen 33 17 0 25 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
Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime Andrea Agazzi Jianfeng Lu 13 15 0 22 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
Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities A. Nassar Y. Yilmaz AI4CE 19 55 0 19 Oct 2020
A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix T. Doan Mehdi Abbana Bennani Bogdan Mazoure Guillaume Rabusseau Pierre Alquier CLL 20 80 0 07 Oct 2020
Computational Separation Between Convolutional and Fully-Connected Networks Eran Malach Shai Shalev-Shwartz 24 26 0 03 Oct 2020
On the linearity of large non-linear models: when and why the tangent kernel is constant Chaoyue Liu Libin Zhu M. Belkin 21 140 0 02 Oct 2020
Neural Thompson Sampling Weitong Zhang Dongruo Zhou Lihong Li Quanquan Gu 28 114 0 02 Oct 2020
Deep Equals Shallow for ReLU Networks in Kernel Regimes A. Bietti Francis R. Bach 28 86 0 30 Sep 2020
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't E. Weinan Chao Ma Stephan Wojtowytsch Lei Wu AI4CE 22 133 0 22 Sep 2020
Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot Jingtong Su Yihang Chen Tianle Cai Tianhao Wu Ruiqi Gao Liwei Wang J. Lee 14 85 0 22 Sep 2020
Tensor Programs III: Neural Matrix Laws Greg Yang 14 43 0 22 Sep 2020