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Pruning methods can considerably reduce the size of artificial neural networks without harming their performance.
Stabilizing the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin · 1903
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Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
D. H. Hubel and T. N. Wiesel · 1962
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Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
Kunihiko Fukushima · 1980
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Mathematical description of the responses of simple cortical cells ∗ \ast
S. Marĉelja · 1980
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Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
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The statistics of natural images
Daniel L Ruderman · 1994
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The early phase of neural network training
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ImageNet Large Scale Visual Recognition Challenge
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Deep residual learning for image recognition
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Perspective: Machine learning potentials for atomistic simulations
Jörg Behler · 2016
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
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Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin · 2019
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V. Le · 2020
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Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
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A downsampled variant of imagenet as an alternative to the CIFAR datasets
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Deep learning for time-series analysis
John Cristian Borges Gamboa · 2017
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Finding the needle in the haystack with convolutions: on the benefits of architectural bias
Stéphane d’Ascoli, Levent Sagun, Giulio Biroli, and Joan Bruna · 2019
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On the transferability of winning tickets in non-natural image datasets
Matthia Sabatelli, Mike Kestemont, and Pierre Geurts · 2020
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Towards learning convolutions from scratch
Behnam Neyshabur · 2020
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Drawing early-bird tickets: Towards 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
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What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
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Rigging the lottery: Making all tickets winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen · 2020
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Dynamic model pruning with feedback
Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, and Martin Jaggi · 2020
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Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
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Mlp-mixer: An all-mlp architecture for vision
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Robustness to pruning predicts generalization in deep neural networks
Lorenz Kuhn, Clare Lyle, Aidan N. Gomez, Jonas Rothfuss, and Yarin Gal · 2021
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