OTOV2: Automatic, Generic, User-Friendly

OTOV2: Automatic, Generic, User-Friendly

13 March 2023

Tianyi Chen

Papers citing "OTOV2: Automatic, Generic, User-Friendly"

7 / 7 papers shown

Title
HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning Tianyi Chen Xiaoyi Qu David Aponte Colby R. Banbury Jongwoo Ko Tianyu Ding Yong Ma Vladimir Lyapunov Ilya Zharkov Luming Liang 31 0 0 11 Sep 2024
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression Yawei Li Shuhang Gu Christoph Mayer Luc Van Gool Radu Timofte 99 176 0 19 Mar 2020
Highly Efficient Salient Object Detection with 100K Parameters Shanghua Gao Yong-qiang Tan Ming-Ming Cheng Chengze Lu Yunpeng Chen Shuicheng Yan 180 147 0 12 Mar 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 204 354 0 05 Mar 2020
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution T. Elsken J. H. Metzen Frank Hutter 90 476 0 24 Apr 2018
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 220 9,849 0 25 Aug 2016
A Proximal Stochastic Gradient Method with Progressive Variance Reduction Lin Xiao Tong Zhang ODL 61 716 0 19 Mar 2014