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Successful adoption of deep learning (DL) in the wild requires models to be: (1) compact, (2) accurate, and (3) robust to distributional shifts.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Ensemble learning
Robi Polikar · 2012
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
Earlier work this paper cites.
Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Deep learning , volume 1
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
Earlier work this paper cites.
Identity mappings in deep residual networks, 2016
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Towards accurate binary convolutional neural network
Xiaofan Lin, Cong Zhao, and Wei Pan · 2017
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Learning sparse neural networks through l _ 0 l\_0 regularization
Christos Louizos, Max Welling, and Diederik P Kingma · 2017
Earlier work this paper cites.
Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
Earlier work this paper cites.
Exploring sparsity in recurrent neural networks
Sharan Narang, Erich Elsen, Gregory Diamos, and Shubho Sengupta · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression, 2017
Michael Zhu and Suyog Gupta · 2017
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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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
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2018
Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin · 2020
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Uncovering the limits of adversarial training against norm-bounded adversarial examples
Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli · 2020
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2020
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Logarithmic pruning is all you need
Laurent Orseau, Marcus Hutter, and Omar Rivasplata · 2020
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What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
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How can we be so dense? the benefits of using highly sparse representations
Subutai Ahmad and Luiz Scheinkman · 2019
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Sipping neural networks: Sensitivity-informed provable pruning of neural networks
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, and Daniela Rus · 2019
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The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
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What do compressed deep neural networks forget?
Sara Hooker, Aaron Courville, Gregory Clark, Yann Dauphin, and Andrea Frome · 2019
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When robustness doesn’t promote robustness: Synthetic vs. natural distribution shifts on imagenet
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2019
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Comparing rewinding and fine-tuning in neural network pruning, 2020
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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Calibrate and prune: Improving reliability of lottery tickets through prediction calibration
Bindya Venkatesh, Jayaraman J Thiagarajan, Kowshik Thopalli, and Prasanna Sattigeri · 2020
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Achieving adversarial robustness via sparsity
Shufan Wang, Ningyi Liao, Liyao Xiang, Nanyang Ye, and Quanshi Zhang · 2020
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Kshitij Bhardwaj and Maya Gokhale · 2021
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Defending against image corruptions through adversarial augmentations
Dan A Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, Andras Gyorgy, Timothy Mann, and Sven Gowal · 2021
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Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning a randomly weighted network
James Diffenderfer and Bhavya Kailkhura · 2021
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On the effectiveness of adversarial training against common corruptions
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Lost in pruning: The effects of pruning neural networks beyond test accuracy
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Optimal lottery tickets via subsetsum: Logarithmic over-parameterization is sufficient, 2021
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Improving adversarial robustness using proxy distributions
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Finding everything within random binary networks, 2021
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Efficientnetv2: Smaller models and faster training
Mingxing Tan and Quoc V Le · 2021
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Emerging paradigms of neural network pruning
Huan Wang, Can Qin, Yulun Zhang, and Yun Fu · 2021
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Can subnetwork structure be the key to out-of-distribution generalization?
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