Fetching the paper…
Reading the bibliography…
Recent work has explored the possibility of pruning neural networks at initialization.
Pruning versus clipping in neural networks
Steven A Janowsky · 1989
Earlier work this paper cites.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
Earlier work this paper cites.
Pruning algorithms-a survey
Russell Reed · 1993
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Sergey Zagoruyko and Nikos Komodakis · 2016
Earlier work this paper cites.
Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
Earlier work this paper cites.
Gradient descent happens in a tiny subspace
Guy Gur-Ari, Daniel A Roberts, and Ethan Dyer · 2018
Earlier work this paper cites.
Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
Earlier work this paper cites.
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 2018
Earlier work this paper cites.
To prune, or not to prune: Exploring the efficacy of pruning for model compression, 2018
Michael H. Zhu and Suyog Gupta · 2018
Cited alongside, same era.
Cerebras wafer scale engine: An introduction, 2019
Cerebras · 2019
Cited alongside, same era.
Sparse networks from scratch: Faster training without losing performance
Tim Dettmers and Luke Zettlemoyer · 2019
Cited alongside, same era.
The difficulty of training sparse neural networks
Utku Evci, Fabian Pedregosa, Aidan Gomez, and Erich Elsen · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Fast sparse convnets
Erich Elsen, Marat Dukhan, Trevor Gale, and Karen Simonyan · 2020
Closest in time.
Rigging the lottery: Making all tickets winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen · 2020
Closest in time.
The break-even point on optimization trajectories of deep neural networks
Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho*, and Krzysztof Geras* · 2020
Closest in time.
A signal propagation perspective for pruning neural networks at initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, and Philip H. S. Torr · 2020
Closest in time.
Finding trainable sparse networks through neural tangent transfer
Tianlin Liu and Friedemann Zenke · 2020
Closest in time.
Nvidia a100 tensor core gpu architecture, 2020
NVIDIA · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
Cited alongside, same era.
SNIP: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr · 2019
Cited alongside, same era.
Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
Cited alongside, same era.
What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
Espn: Extremely sparse pruned networks
Minsu Cho, Ameya Joshi, and Chinmay Hegde · 2020
Cited alongside, same era.
Progressive skeletonization: Trimming more fat from a network at initialization
Pau de Jorge, Amartya Sanyal, Harkirat S Behl, Philip HS Torr, Gregory Rogez, and Puneet K Dokania · 2020
Cited alongside, same era.
Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
Closest in time.
Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel LK Yamins, and Surya Ganguli · 2020
Closest in time.
Introducing 2nd generation ipu systems for ai at scale, 2020
Nigel Toon · 2020
Closest in time.
Pruning via iterative ranking of sensitivity statistics
Stijn Verdenius, Maarten Stol, and Patrick Forré · 2020
Closest in time.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
Closest in time.
Drawing early-bird tickets: Toward more efficient training of deep networks
Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, and Yingyan Lin · 2020
Closest in time.