Progressive Skeletonization: Trimming more fat from a network at initialization

16 June 2020

Papers citing "Progressive Skeletonization: Trimming more fat from a network at initialization"

10 / 60 papers shown

Title
Signing the Supermask: Keep, Hide, Invert Nils Koster O. Grothe Achim Rettinger 23 10 0 31 Jan 2022
When to Prune? A Policy towards Early Structural Pruning Maying Shen Pavlo Molchanov Hongxu Yin J. Álvarez VLM 20 52 0 22 Oct 2021
HALP: Hardware-Aware Latency Pruning Maying Shen Hongxu Yin Pavlo Molchanov Lei Mao Jianna Liu J. Álvarez VLM 30 13 0 20 Oct 2021
Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity Artem Vysogorets Julia Kempe 13 19 0 05 Jul 2021
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration Shiwei Liu Tianlong Chen Xiaohan Chen Zahra Atashgahi Lu Yin Huanyu Kou Li Shen Mykola Pechenizkiy Zhangyang Wang D. Mocanu 34 111 0 19 Jun 2021
Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset Ilan Price Jared Tanner 27 15 0 12 Feb 2021
Pruning Neural Networks at Initialization: Why are We Missing the Mark? Jonathan Frankle Gintare Karolina Dziugaite Daniel M. Roy Michael Carbin 6 238 0 18 Sep 2020
Pruning neural networks without any data by iteratively conserving synaptic flow Hidenori Tanaka D. Kunin Daniel L. K. Yamins Surya Ganguli 8 628 0 09 Jun 2020
A Brain-inspired Algorithm for Training Highly Sparse Neural Networks Zahra Atashgahi Joost Pieterse Shiwei Liu D. Mocanu Raymond N. J. Veldhuis Mykola Pechenizkiy 21 15 0 17 Mar 2019
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 264 5,326 0 05 Nov 2016