Computational Separation Between Convolutional and Fully-Connected Networks

3 October 2020

Papers citing "Computational Separation Between Convolutional and Fully-Connected Networks"

20 / 20 papers shown

Title
A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts Mohammed Nowaz Rabbani Chowdhury Meng Wang K. E. Maghraoui Naigang Wang Pin-Yu Chen Christopher Carothers MoE 31 4 0 26 May 2024
Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs Aakash Lahoti Stefani Karp Ezra Winston Aarti Singh Yuanzhi Li 21 0 0 23 Mar 2024
Kernels, Data & Physics Francesco Cagnetta Deborah Oliveira Mahalakshmi Sabanayagam Nikolaos Tsilivis Julia Kempe 20 0 0 05 Jul 2023
Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs Emmanuel Abbe Elisabetta Cornacchia Aryo Lotfi 26 11 0 29 Jun 2023
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks Mohammed Nowaz Rabbani Chowdhury Shuai Zhang M. Wang Sijia Liu Pin-Yu Chen MoE 21 17 0 07 Jun 2023
A Mathematical Model for Curriculum Learning for Parities Elisabetta Cornacchia Elchanan Mossel 32 10 0 31 Jan 2023
A Kernel Perspective of Skip Connections in Convolutional Networks Daniel Barzilai Amnon Geifman Meirav Galun Ronen Basri 15 11 0 27 Nov 2022
On the non-universality of deep learning: quantifying the cost of symmetry Emmanuel Abbe Enric Boix-Adserà FedML MLT 22 18 0 05 Aug 2022
What Can Be Learnt With Wide Convolutional Neural Networks? Francesco Cagnetta Alessandro Favero M. Wyart MLT 25 11 0 01 Aug 2022
An initial alignment between neural network and target is needed for gradient descent to learn Emmanuel Abbe Elisabetta Cornacchia Jan Hązła Christopher Marquis 16 16 0 25 Feb 2022
Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks Lechao Xiao 26 21 0 10 Dec 2021
Learning with convolution and pooling operations in kernel methods Theodor Misiakiewicz Song Mei MLT 13 29 0 16 Nov 2021
On the Power of Differentiable Learning versus PAC and SQ Learning Emmanuel Abbe Pritish Kamath Eran Malach Colin Sandon Nathan Srebro MLT 69 23 0 09 Aug 2021
Locality defeats the curse of dimensionality in convolutional teacher-student scenarios Alessandro Favero Francesco Cagnetta M. Wyart 22 31 0 16 Jun 2021
On the Sample Complexity of Learning under Invariance and Geometric Stability A. Bietti Luca Venturi Joan Bruna 22 5 0 14 Jun 2021
Video Super-Resolution Transformer Jie Cao Yawei Li K. Zhang Luc Van Gool ViT 28 166 0 12 Jun 2021
Approximation and Learning with Deep Convolutional Models: a Kernel Perspective A. Bietti 22 29 0 19 Feb 2021
Towards Learning Convolutions from Scratch Behnam Neyshabur SSL 218 71 0 27 Jul 2020
On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location O. Kayhan J. C. V. Gemert 209 232 0 16 Mar 2020
Convolution by Evolution: Differentiable Pattern Producing Networks Chrisantha Fernando Dylan Banarse Malcolm Reynolds F. Besse David Pfau Max Jaderberg Marc Lanctot Daan Wierstra 191 102 0 08 Jun 2016