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When Layers Play the Lottery, all Tickets Win at Initialization

When Layers Play the Lottery, all Tickets Win at Initialization

25 January 2023
Artur Jordão
George Correa de Araujo
H. Maia
Hélio Pedrini
ArXivPDFHTML

Papers citing "When Layers Play the Lottery, all Tickets Win at Initialization"

17 / 17 papers shown
Title
DropNet: Reducing Neural Network Complexity via Iterative Pruning
DropNet: Reducing Neural Network Complexity via Iterative Pruning
Chong Min John Tan
Mehul Motani
55
55
0
14 Jul 2022
Attention is Not All You Need: Pure Attention Loses Rank Doubly
  Exponentially with Depth
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Yihe Dong
Jean-Baptiste Cordonnier
Andreas Loukas
115
385
0
05 Mar 2021
Dynamic Neural Networks: A Survey
Dynamic Neural Networks: A Survey
Yizeng Han
Gao Huang
Shiji Song
Le Yang
Honghui Wang
Yulin Wang
3DH
AI4TS
AI4CE
109
649
0
09 Feb 2021
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch
Aojun Zhou
Yukun Ma
Junnan Zhu
Jianbo Liu
Zhijie Zhang
Kun Yuan
Wenxiu Sun
Hongsheng Li
196
247
0
08 Feb 2021
Progressive Skeletonization: Trimming more fat from a network at
  initialization
Progressive Skeletonization: Trimming more fat from a network at initialization
Pau de Jorge
Amartya Sanyal
Harkirat Singh Behl
Philip Torr
Grégory Rogez
P. Dokania
57
95
0
16 Jun 2020
What is the State of Neural Network Pruning?
What is the State of Neural Network Pruning?
Davis W. Blalock
Jose Javier Gonzalez Ortiz
Jonathan Frankle
John Guttag
262
1,050
0
06 Mar 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Comparing Rewinding and Fine-tuning in Neural Network Pruning
Alex Renda
Jonathan Frankle
Michael Carbin
265
387
0
05 Mar 2020
HRank: Filter Pruning using High-Rank Feature Map
HRank: Filter Pruning using High-Rank Feature Map
Mingbao Lin
Rongrong Ji
Yan Wang
Yichen Zhang
Baochang Zhang
Yonghong Tian
Ling Shao
67
725
0
24 Feb 2020
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with
  Pattern-based Weight Pruning
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
Wei Niu
Xiaolong Ma
Sheng Lin
Shihao Wang
Xuehai Qian
Xinyu Lin
Yanzhi Wang
Bin Ren
MQ
75
228
0
01 Jan 2020
Neural Network Pruning with Residual-Connections and Limited-Data
Neural Network Pruning with Residual-Connections and Limited-Data
Jian-Hao Luo
Jianxin Wu
61
114
0
19 Nov 2019
Quantifying the Carbon Emissions of Machine Learning
Quantifying the Carbon Emissions of Machine Learning
Alexandre Lacoste
A. Luccioni
Victor Schmidt
Thomas Dandres
88
703
0
21 Oct 2019
Reducing Transformer Depth on Demand with Structured Dropout
Reducing Transformer Depth on Demand with Structured Dropout
Angela Fan
Edouard Grave
Armand Joulin
115
592
0
25 Sep 2019
Rethinking the Value of Network Pruning
Rethinking the Value of Network Pruning
Zhuang Liu
Mingjie Sun
Tinghui Zhou
Gao Huang
Trevor Darrell
36
1,471
0
11 Oct 2018
SNIP: Single-shot Network Pruning based on Connection Sensitivity
SNIP: Single-shot Network Pruning based on Connection Sensitivity
Namhoon Lee
Thalaiyasingam Ajanthan
Philip Torr
VLM
252
1,199
0
04 Oct 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle
Michael Carbin
225
3,463
0
09 Mar 2018
Deep Networks with Stochastic Depth
Deep Networks with Stochastic Depth
Gao Huang
Yu Sun
Zhuang Liu
Daniel Sedra
Kilian Q. Weinberger
212
2,356
0
30 Mar 2016
EIE: Efficient Inference Engine on Compressed Deep Neural Network
EIE: Efficient Inference Engine on Compressed Deep Neural Network
Song Han
Xingyu Liu
Huizi Mao
Jing Pu
A. Pedram
M. Horowitz
W. Dally
118
2,456
0
04 Feb 2016
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