Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers

12 November 2018

Papers citing "Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers"

50 / 498 papers shown

Title
RATT: Leveraging Unlabeled Data to Guarantee Generalization Saurabh Garg Sivaraman Balakrishnan J. Zico Kolter Zachary Chase Lipton 32 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
Achieving Small Test Error in Mildly Overparameterized Neural Networks Shiyu Liang Ruoyu Sun R. Srikant 20 3 0 24 Apr 2021
A Recipe for Global Convergence Guarantee in Deep Neural Networks Kenji Kawaguchi Qingyun Sun 27 11 0 12 Apr 2021
Noether: The More Things Change, the More Stay the Same Grzegorz Gluch R. Urbanke 22 17 0 12 Apr 2021
Training Deep Neural Networks via Branch-and-Bound Yuanwei Wu Ziming Zhang Guanghui Wang ODL 25 0 0 05 Apr 2021
Learning with Neural Tangent Kernels in Near Input Sparsity Time A. Zandieh 9 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 34 13 0 01 Apr 2021
Fitting Elephants P. Mitra 11 0 0 31 Mar 2021
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks Yuqing Li Tao Luo Chao Ma CML 16 1 0 30 Mar 2021
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks Atish Agarwala Abhimanyu Das Brendan Juba Rina Panigrahy Vatsal Sharan Xin Wang Qiuyi Zhang MoMe 9 10 0 29 Mar 2021
A Temporal Kernel Approach for Deep Learning with Continuous-time Information Da Xu Chuanwei Ruan Evren Körpeoglu Sushant Kumar Kannan Achan SyDa AI4TS 22 4 0 28 Mar 2021
Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives Javier Del Ser D. Casillas-Pérez L. Cornejo-Bueno Luis Prieto-Godino J. Sanz-Justo C. Casanova-Mateo S. Salcedo-Sanz AI4CE 42 42 0 26 Mar 2021
Weighted Neural Tangent Kernel: A Generalized and Improved Network-Induced Kernel Lei Tan Shutong Wu Xiaolin Huang 26 1 0 22 Mar 2021
For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions Nishanth Dikkala Gal Kaplun Rina Panigrahy 14 6 0 11 Mar 2021
Recent Advances on Neural Network Pruning at Initialization Huan Wang Can Qin Yue Bai Yulun Zhang Yun Fu CVBM 33 64 0 11 Mar 2021
Robustness to Pruning Predicts Generalization in Deep Neural Networks Lorenz Kuhn Clare Lyle Aidan Gomez Jonas Rothfuss Y. Gal 43 14 0 10 Mar 2021
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models Peizhong Ju Xiaojun Lin Ness B. Shroff MLT 29 10 0 09 Mar 2021
Don't Forget to Sign the Gradients! Omid Aramoon Pin-Yu Chen Gang Qu 11 5 0 05 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
Shift Invariance Can Reduce Adversarial Robustness Songwei Ge Vasu Singla Ronen Basri David Jacobs AAML OOD 18 26 0 03 Mar 2021
Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation Semih Cayci Siddhartha Satpathi Niao He F. I. R. Srikant 29 9 0 02 Mar 2021
Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels Eran Malach Pritish Kamath Emmanuel Abbe Nathan Srebro 15 39 0 01 Mar 2021
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 55 89 0 25 Feb 2021
Understanding Robustness in Teacher-Student Setting: A New Perspective Zhuolin Yang Zhaoxi Chen Tiffany Cai Xinyun Chen Bo-wen Li Yuandong Tian AAML 35 2 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
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
A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space Kuo Gai Shihua Zhang 34 6 0 18 Feb 2021
Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent Ajaykrishna Karthikeyan Naman Jain Nagarajan Natarajan Prateek Jain MQ 21 13 0 15 Feb 2021
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers Kenji Kawaguchi PINN 28 42 0 15 Feb 2021
Generalization error of random features and kernel methods: hypercontractivity and kernel matrix concentration Song Mei Theodor Misiakiewicz Andrea Montanari 14 110 0 26 Jan 2021
Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation Yuki Fujimoto K. Fukushima K. Murase BDL 19 26 0 20 Jan 2021
Knowledge Distillation Methods for Efficient Unsupervised Adaptation Across Multiple Domains Le Thanh Nguyen-Meidine Atif Belal M. Kiran Jose Dolz Louis-Antoine Blais-Morin Eric Granger 33 25 0 18 Jan 2021
Provably Training Overparameterized Neural Network Classifiers with Non-convex Constraints You-Lin Chen Zhaoran Wang Mladen Kolar 19 0 0 30 Dec 2020
Mathematical Models of Overparameterized Neural Networks Cong Fang Hanze Dong Tong Zhang 32 22 0 27 Dec 2020
Improving the Generalization of End-to-End Driving through Procedural Generation Quanyi Li Zhenghao Peng Qihang Zhang Chunxiao Liu Bolei Zhou 18 16 0 26 Dec 2020
Recent advances in deep learning theory Fengxiang He Dacheng Tao AI4CE 24 50 0 20 Dec 2020
Recent Theoretical Advances in Non-Convex Optimization Marina Danilova Pavel Dvurechensky Alexander Gasnikov Eduard A. Gorbunov Sergey Guminov Dmitry Kamzolov Innokentiy Shibaev 33 77 0 11 Dec 2020
On 1/n neural representation and robustness Josue Nassar Piotr A. Sokól SueYeon Chung K. Harris Il Memming Park AAML OOD 24 23 0 08 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 12 15 0 04 Dec 2020
Neural Contextual Bandits with Deep Representation and Shallow Exploration Pan Xu Zheng Wen Handong Zhao Quanquan Gu OffRL 27 72 0 03 Dec 2020
Tight Hardness Results for Training Depth-2 ReLU Networks Surbhi Goel Adam R. Klivans Pasin Manurangsi Daniel Reichman 16 40 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
Coresets for Robust Training of Neural Networks against Noisy Labels Baharan Mirzasoleiman Kaidi Cao J. Leskovec NoLa 19 32 0 15 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
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
Kernel Dependence Network Chieh-Tsai Wu A. Masoomi Arthur Gretton Jennifer Dy 16 0 0 04 Nov 2020
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks Minshuo Chen Hao Liu Wenjing Liao T. Zhao CML OOD OffRL 15 7 0 03 Nov 2020
DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks Shiyun Xu Zhiqi Bu 6 1 0 01 Nov 2020