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Sparse neural networks attract increasing interest as they exhibit comparable performance to their dense counterparts while being computationally efficient.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Spoken letter recognition
Mark Fanty and Ronald Cole · 1991
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Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G Stork · 1993
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Newsweeder: Learning to filter netnews
Ken Lang · 1995
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The mnist database of handwritten digits
Yann LeCun · 1998
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The organization of behavior: A neuropsychological theory
Donald Olding Hebb · 2005
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Hierarchical models in the brain
Karl Friston · 2008
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Feature extraction: foundations and applications , volume 207
Isabelle Guyon, Steve Gunn, Masoud Nikravesh, and Lofti A Zadeh · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Cosine similarity metric learning for face verification
Hieu V Nguyen and Li Bai · 2010
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Getting to know your data
Jiawei Han, Micheline Kamber, Jian Pei, et al · 2012
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Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
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Distance weighted cosine similarity measure for text classification
Baoli Li and Liping Han · 2013
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Provable bounds for learning some deep representations
Sanjeev Arora, Aditya Bhaskara, Rong Ge, and Tengyu Ma · 2014
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Soft similarity and soft cosine measure: Similarity of features in vector space model
Grigori Sidorov, Alexander Gelbukh, Helena Gómez-Adorno, and David Pinto · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William J Dally · 2015
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Biological context of hebb learning in artificial neural networks, a review
Eduard Kuriscak, Petr Marsalek, Julius Stroffek, and Peter G Toth · 2015
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Recurrent convolutional neural network for object recognition
Ming Liang and Xiaolin Hu · 2015
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Learning similarity with cosine similarity ensemble
Peipei Xia, Li Zhang, and Fanzhang Li · 2015
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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A topological insight into restricted boltzmann machines
Decebal Constantin Mocanu, Elena Mocanu, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 2016
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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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Towards a biologically plausible backprop
Benjamin Scellier and Yoshua Bengio · 2016
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Sparsifying neural network connections for face recognition
Yi Sun, Xiaogang Wang, and Xiaoou Tang · 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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Deep learning scaling is predictable, empirically
Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Patwary, Mostofa Ali, Yang Yang, and Yanqi Zhou · 2017
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In-datacenter performance analysis of a tensor processing unit
Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al · 2017
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Modeling hebb learning rule for unsupervised learning
Jia Liu, Maoguo Gong, and Qiguang Miao · 2017
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Importance estimation for neural network pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz · 2019
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Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Hesham Mostafa and Xin Wang · 2019
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The role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro · 2019
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Neural oblivious decision ensembles for deep learning on tabular data
Sergei Popov, Stanislav Morozov, and Artem Babenko · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D 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 Ziegler, Jeffrey Wu, Clemens Winter, Chris 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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Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
Cited alongside, same era.
Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Sergey Bartunov, Adam Santoro, Blake A Richards, Luke Marris, Geoffrey E Hinton, and Timothy P Lillicrap · 2018
Cited alongside, same era.
Deep rewiring: Training very sparse deep networks
Guillaume Bellec, David Kappel, Wolfgang Maass, and Robert Legenstein · 2018
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
Cited alongside, same era.
Morphnet: Fast & simple resource-constrained structure learning of deep networks
Ariel Gordon, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, and Edward Choi · 2018
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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
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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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Assessment list for trustworthy artificial intelligence (ALTAI) for self-assessment, 2020
AI High-Level Expert Group · 2020
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Top-kast: Top-k always sparse training
Siddhant Jayakumar, Razvan Pascanu, Jack Rae, Simon Osindero, and Erich Elsen · 2020
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Soft threshold weight reparameterization for learnable sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, and Ali Farhadi · 2020
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Group sparsity: The hinge between filter pruning and decomposition for network compression
Yawei Li, Shuhang Gu, Christoph Mayer, Luc Van Gool, and Radu Timofte · 2020
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Topological insights into sparse neural networks
Shiwei Liu, Tim van der Lee, Anil Yaman, Zahra Atashgahi, Davide Ferraro, Ghada Sokar, Mykola Pechenizkiy, and Decebal Constantin Mocanu · 2020
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Sparse weight activation training
Md Aamir Raihan and Tor M Aamodt · 2020
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Winning the lottery with continuous sparsification
Pedro Savarese, Hugo Silva, and Michael Maire · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L Yamins, and Surya Ganguli · 2020
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Deep learning in the era of edge computing: Challenges and opportunities
Mi Zhang, Faen Zhang, Nicholas D Lane, Yuanchao Shu, Xiao Zeng, Biyi Fang, Shen Yan, and Hui Xu · 2020
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Lukas Galke and Ansgar Scherp · 2021
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Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
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Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
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Sparse training theory for scalable and efficient agents
Decebal Constantin Mocanu, Elena Mocanu, Tiago Pinto, Selima Curci, Phuong H Nguyen, Madeleine Gibescu, Damien Ernst, and Zita A Vale · 2021
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Towards biologically plausible convolutional networks
Roman Pogodin, Yash Mehta, Timothy P Lillicrap, and Peter E Latham · 2021
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Livewired neural networks: Making neurons that fire together wire together
Thomas Schumacher · 2021
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Mlp-mixer: An all-mlp architecture for vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, et al · 2021
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Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders
Zahra Atashgahi, Ghada Sokar, Tim van der Lee, Elena Mocanu, Decebal Constantin Mocanu, Raymond Veldhuis, and Mykola Pechenizkiy · 2022
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