Deep Networks from the Principle of Rate Reduction

Deep Networks from the Principle of Rate Reduction

27 October 2020

Kwan Ho Ryan Chan

Haozhi Qi

Papers citing "Deep Networks from the Principle of Rate Reduction"

8 / 8 papers shown

Title
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold Can Yaras Peng Wang Zhihui Zhu Laura Balzano Qing Qu 25 41 0 19 Sep 2022
Neural Collapse: A Review on Modelling Principles and Generalization Vignesh Kothapalli 25 71 0 08 Jun 2022
Recent Advances of Continual Learning in Computer Vision: An Overview Haoxuan Qu Hossein Rahmani Li Xu Bryan M. Williams Jun Liu VLM CLL 25 73 0 23 Sep 2021
A Geometric Analysis of Neural Collapse with Unconstrained Features Zhihui Zhu Tianyu Ding Jinxin Zhou Xiao Li Chong You Jeremias Sulam Qing Qu 27 194 0 06 May 2021
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs Thomas O'Leary-Roseberry Umberto Villa Peng Chen Omar Ghattas 47 68 0 30 Nov 2020
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 297 10,225 0 16 Nov 2016
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 271 5,327 0 05 Nov 2016
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding Vardan Papyan Yaniv Romano Michael Elad 59 284 0 27 Jul 2016