Deep Expander Networks: Efficient Deep Networks from Graph Theory

23 November 2017

Papers citing "Deep Expander Networks: Efficient Deep Networks from Graph Theory"

11 / 11 papers shown

Title
Brain-inspired sparse training enables Transformers and LLMs to perform as fully connected Yingtao Zhang Jialin Zhao Wenjing Wu Ziheng Liao Umberto Michieli C. Cannistraci 48 0 0 31 Jan 2025
Graph Expansion in Pruned Recurrent Neural Network Layers Preserve Performance Suryam Arnav Kalra Arindam Biswas Pabitra Mitra Biswajit Basu GNN 31 0 0 17 Mar 2024
Expander Graph Propagation Andreea Deac Marc Lackenby Petar Velivcković 96 52 0 06 Oct 2022
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance Shiwei Liu Yuesong Tian Tianlong Chen Li Shen 34 8 0 05 Mar 2022
Naming Schema for a Human Brain-Scale Neural Network Morgan Schaefer Lauren Michelin J. Kepner 3DH 14 2 0 22 Sep 2021
Partitioning sparse deep neural networks for scalable training and inference G. Demirci Hakan Ferhatosmanoglu 18 11 0 23 Apr 2021
Sparse Training Theory for Scalable and Efficient Agents D. Mocanu Elena Mocanu T. Pinto Selima Curci Phuong H. Nguyen M. Gibescu D. Ernst Z. Vale 45 17 0 02 Mar 2021
Graph Structure of Neural Networks Jiaxuan You J. Leskovec Kaiming He Saining Xie GNN 19 136 0 13 Jul 2020
Pre-Defined Sparse Neural Networks with Hardware Acceleration Sourya Dey Kuan-Wen Huang P. Beerel K. Chugg 41 24 0 04 Dec 2018
Dense xUnit Networks I. Kligvasser T. Michaeli 13 3 0 27 Nov 2018
Sparsely Aggregated Convolutional Networks Ligeng Zhu Ruizhi Deng Michael Maire Zhiwei Deng Greg Mori P. Tan 3DPC 30 9 0 18 Jan 2018