Compressing Neural Networks: Towards Determining the Optimal Layer-wise
Decomposition

Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition

23 July 2021

Lucas Liebenwein

Daniela Rus

Papers citing "Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition"

8 / 8 papers shown

Title
Structure-Preserving Network Compression Via Low-Rank Induced Training Through Linear Layers Composition Xitong Zhang Ismail R. Alkhouri Rongrong Wang 31 0 0 06 May 2024
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models Jinqi Xiao Miao Yin Yu Gong Xiao Zang Jian Ren Bo Yuan VLM ViT 30 9 0 26 May 2023
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks Jinqi Xiao Chengming Zhang Yu Gong Miao Yin Yang Sui Lizhi Xiang Dingwen Tao Bo Yuan 16 19 0 20 Jan 2023
Deep Learning on Home Drone: Searching for the Optimal Architecture Alaa Maalouf Yotam Gurfinkel Barak Diker O. Gal Daniela Rus Dan Feldman 10 5 0 21 Sep 2022
SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search Zhewen Yu C. Bouganis 8 4 0 22 Aug 2022
Compression-aware Training of Neural Networks using Frank-Wolfe Max Zimmer Christoph Spiegel S. Pokutta 14 9 0 24 May 2022
Sparse Flows: Pruning Continuous-depth Models Lucas Liebenwein Ramin Hasani Alexander Amini Daniela Rus 11 16 0 24 Jun 2021
Dimensionality Reduction via Regression in Hyperspectral Imagery Valero Laparra Jesús Malo Gustau Camps-Valls 30 42 0 31 Jan 2016