Diverse Neural Network Learns True Target Functions

9 November 2016

Papers citing "Diverse Neural Network Learns True Target Functions"

40 / 40 papers shown

Title
WLD-Reg: A Data-dependent Within-layer Diversity Regularizer Firas Laakom Jenni Raitoharju Alexandros Iosifidis Moncef Gabbouj AI4CE 29 7 0 03 Jan 2023
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off Shaoyi Huang Bowen Lei Dongkuan Xu Hongwu Peng Yue Sun Mimi Xie Caiwen Ding 29 19 0 30 Nov 2022
Characterizing the Spectrum of the NTK via a Power Series Expansion Michael Murray Hui Jin Benjamin Bowman Guido Montúfar 40 11 0 15 Nov 2022
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work Jiawei Zhang Yushun Zhang Mingyi Hong Ruoyu Sun Zhi-Quan Luo 31 10 0 21 Oct 2022
Learning Diversified Feature Representations for Facial Expression Recognition in the Wild Negar Heidari Alexandros Iosifidis CVBM 37 3 0 17 Oct 2022
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy Tianlong Chen Zhenyu Zhang Yu Cheng Ahmed Hassan Awadallah Zhangyang Wang ViT 41 37 0 12 Mar 2022
Benefit of Interpolation in Nearest Neighbor Algorithms Yue Xing Qifan Song Guang Cheng 17 28 0 23 Feb 2022
Improved Overparametrization Bounds for Global Convergence of Stochastic Gradient Descent for Shallow Neural Networks Bartlomiej Polaczyk J. Cyranka ODL 35 3 0 28 Jan 2022
Overview frequency principle/spectral bias in deep learning Z. Xu Tao Luo Yaoyu Zhang FaML 35 66 0 19 Jan 2022
Rethinking Influence Functions of Neural Networks in the Over-parameterized Regime Rui Zhang Shihua Zhang TDI 29 21 0 15 Dec 2021
Fast Sketching of Polynomial Kernels of Polynomial Degree Zhao Song David P. Woodruff Zheng Yu Lichen Zhang 21 40 0 21 Aug 2021
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent Spencer Frei Quanquan Gu 26 26 0 25 Jun 2021
Learning distinct features helps, provably Firas Laakom Jenni Raitoharju Alexandros Iosifidis Moncef Gabbouj MLT 36 6 0 10 Jun 2021
Learning with Hyperspherical Uniformity Weiyang Liu Rongmei Lin Zhen Liu Li Xiong Bernhard Schölkopf Adrian Weller 37 35 0 02 Mar 2021
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning Zeyuan Allen-Zhu Yuanzhi Li FedML 62 357 0 17 Dec 2020
Deep Equals Shallow for ReLU Networks in Kernel Regimes A. Bietti Francis R. Bach 32 85 0 30 Sep 2020
When Does Preconditioning Help or Hurt Generalization? S. Amari Jimmy Ba Roger C. Grosse Xuechen Li Atsushi Nitanda Taiji Suzuki Denny Wu Ji Xu 36 32 0 18 Jun 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 39 147 0 20 May 2020
Orthogonal Over-Parameterized Training Weiyang Liu Rongmei Lin Zhen Liu James M. Rehg Liam Paull Li Xiong Le Song Adrian Weller 32 41 0 09 Apr 2020
Symmetry & critical points for a model shallow neural network Yossi Arjevani M. Field 36 13 0 23 Mar 2020
Frequency Bias in Neural Networks for Input of Non-Uniform Density Ronen Basri Meirav Galun Amnon Geifman David Jacobs Yoni Kasten S. Kritchman 45 183 0 10 Mar 2020
Learning Parities with Neural Networks Amit Daniely Eran Malach 24 76 0 18 Feb 2020
Benign Overfitting in Linear Regression Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler MLT 8 763 0 26 Jun 2019
Improving Neural Language Modeling via Adversarial Training Dilin Wang Chengyue Gong Qiang Liu AAML 43 115 0 10 Jun 2019
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks Guodong Zhang James Martens Roger C. Grosse ODL 22 124 0 27 May 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? Zeyuan Allen-Zhu Yuanzhi Li 24 183 0 24 May 2019
Fine-grained Optimization of Deep Neural Networks Mete Ozay ODL 16 1 0 22 May 2019
Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skip-connections E. Weinan Chao Ma Qingcan Wang Lei Wu MLT 37 22 0 10 Apr 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 47 351 0 27 Mar 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 55 961 0 24 Jan 2019
Non-attracting Regions of Local Minima in Deep and Wide Neural Networks Henning Petzka C. Sminchisescu 29 9 0 16 Dec 2018
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 41 765 0 12 Nov 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 65 1,252 0 04 Oct 2018
Learning One-hidden-layer ReLU Networks via Gradient Descent Xiao Zhang Yaodong Yu Lingxiao Wang Quanquan Gu MLT 30 134 0 20 Jun 2018
Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex Hongyang R. Zhang Junru Shao Ruslan Salakhutdinov 39 14 0 06 Jun 2018
Visualizing the Loss Landscape of Neural Nets Hao Li Zheng Xu Gavin Taylor Christoph Studer Tom Goldstein 111 1,850 0 28 Dec 2017
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks Mahdi Soltanolkotabi Adel Javanmard Jason D. Lee 36 415 0 16 Jul 2017
Global optimality conditions for deep neural networks Chulhee Yun S. Sra Ali Jadbabaie 128 118 0 08 Jul 2017
Deep Semi-Random Features for Nonlinear Function Approximation Kenji Kawaguchi Bo Xie Vikas Verma Le Song 29 15 0 28 Feb 2017
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 186 1,186 0 30 Nov 2014