Papers that use sparsity in deep learning

This is a list of papers curated for the paper “Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks”.

The following list is automatically generated from sparsity.bib. To contribute to this list, please set up a Pull Request and add new bibtex entries.

Papers

Achille, Alessandro, Matteo Rovere, and Stefano Soatto. 2019. “Critical Learning Periods in Deep Neural Networks.” http://arxiv.org/abs/1711.08856.

Afghan, Sher, and Uwe Naumann. 2020. “Interval Adjoint Significance Analysis for Neural Networks.” In International Conference on Computational Science, 365–78. Springer.

Aghasi, Alireza, Afshin Abdi, Nam Nguyen, and Justin Romberg. 2017. “Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee.” http://arxiv.org/abs/1611.05162.

Ahmad, Subutai, and Luiz Scheinkman. 2019. “How Can We Be so Dense? The Benefits of Using Highly Sparse Representations.” http://arxiv.org/abs/1903.11257.

Aji, Alham Fikriand, and Kenneth Heafield. 2017. “Sparse Communication for Distributed Gradient Descent.” In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 440–45. http://arxiv.org/abs/1704.05021.

Albericio, J., P. Judd, T. Hetherington, T. Aamodt, N. E. Jerger, and A. Moshovos. 2016. “Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing.” In 2016 Acm/Ieee 43rd Annual International Symposium on Computer Architecture (Isca), 1–13. https://doi.org/10.1109/ISCA.2016.11.

Alistarh, Dan, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic. 2017. “QSGD: Communication-Efficient Sgd via Gradient Quantization and Encoding.” http://arxiv.org/abs/1610.02132.

Alistarh, Dan, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, and Cédric Renggli. 2018. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems, 5973–83. http://arxiv.org/abs/1809.10505.

Allen-Zhu, Zeyuan, Yuanzhi Li, and Zhao Song. 2019. “A Convergence Theory for Deep Learning via over-Parameterization.” http://arxiv.org/abs/1811.03962.

Almahairi, Amjad, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, and Aaron Courville. 2016. “Dynamic Capacity Networks.” http://arxiv.org/abs/1511.07838.

Alvarez, Jose M., and Mathieu Salzmann. 2017. “Compression-Aware Training of Deep Networks.” http://arxiv.org/abs/1711.02638.

Alwani, Manoj, Han Chen, Michael Ferdman, and Peter Milder. 2016. “Fused-Layer Cnn Accelerators.” In The 49th Annual Ieee/Acm International Symposium on Microarchitecture, 22. IEEE Press.

Amari, Shun-ichi. 1998. “Natural Gradient Works Efficiently in Learning.” Neural Computation 10 (2): 251–76. https://doi.org/10.1162/089976698300017746.

Anwar, Sajid, Kyuyeon Hwang, and Wonyong Sung. 2017. “Structured Pruning of Deep Convolutional Neural Networks.” ACM Journal on Emerging Technologies in Computing Systems (JETC) 13 (3): 1–18.

Atashgahi, Zahra, Ghada Sokar, Tim van der Lee, Elena Mocanu, Decebal Constantin Mocanu, Raymond Veldhuis, and Mykola Pechenizkiy. 2020. “Quick and Robust Feature Selection: The Strength of Energy-Efficient Sparse Training for Autoencoders.” http://arxiv.org/abs/2012.00560.

Azarian, Kambiz, Yash Bhalgat, Jinwon Lee, and Tijmen Blankevoort. 2020. “Learned Threshold Pruning.” http://arxiv.org/abs/2003.00075.

Ba, Jimmy, Roger Grosse, and James Martens. 2016. “Distributed Second-Order Optimization Using Kronecker-Factored Approximations.”

Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. “Layer Normalization.” http://arxiv.org/abs/1607.06450.

Baalen, Mart van, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, and Max Welling. 2020. “Bayesian Bits: Unifying Quantization and Pruning.” http://arxiv.org/abs/2005.07093.

Baldi, Pierre, and Peter J Sadowski. 2013. “Understanding Dropout.” In Advances in Neural Information Processing Systems, edited by C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, 26:2814–22. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2013/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf.

Bartoldson, Brian R., Ari S. Morcos, Adrian Barbu, and Gordon Erlebacher. 2020. “The Generalization-Stability Tradeoff in Neural Network Pruning.” http://arxiv.org/abs/1906.03728.

Basu, Debraj, Deepesh Data, Can Karakus, and Suhas N Diggavi. 2020. “Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations.” IEEE Journal on Selected Areas in Information Theory 1 (1): 217–26. http://arxiv.org/abs/1906.02367.

Baykal, Cenk, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, and Daniela Rus. 2018. “Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds.” arXiv Preprint arXiv:1804.05345.

Beck, Amir, and Marc Teboulle. 2009. “A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems.” SIAM J. Img. Sci. 2 (1): 183–202. https://doi.org/10.1137/080716542.

Bellec, Guillaume, David Kappel, Wolfgang Maass, and Robert Legenstein. 2018. “Deep Rewiring: Training Very Sparse Deep Networks.” http://arxiv.org/abs/1711.05136.

Beltagy, Iz, Matthew E. Peters, and Arman Cohan. 2020. “Longformer: The Long-Document Transformer.” http://arxiv.org/abs/2004.05150.

Bengio, Emmanuel, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup. 2016. “Conditional Computation in Neural Networks for Faster Models.” http://arxiv.org/abs/1511.06297.

Bengio, Yoshua, Nicholas Léonard, and Aaron Courville. 2013. “Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.” http://arxiv.org/abs/1308.3432.

Ben-Nun, Tal, Maciej Besta, Simon Huber, Alexandros Nikolaos Ziogas, Daniel Peter, and Torsten Hoefler. 2019. “A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning.” http://arxiv.org/abs/1901.10183.

Ben-Nun, Tal, and Torsten Hoefler. 2018. “Demystifying Parallel and Distributed Deep Learning: An in-Depth Concurrency Analysis.” http://arxiv.org/abs/1802.09941.

Betzel, Richard F, John D Medaglia, Lia Papadopoulos, Graham L Baum, Ruben Gur, Raquel Gur, David Roalf, Theodore D Satterthwaite, and Danielle S Bassett. 2017. “The Modular Organization of Human Anatomical Brain Networks: Accounting for the Cost of Wiring.” Network Neuroscience 1 (1): 42–68.

Bianco, Simone, Remi Cadene, Luigi Celona, and Paolo Napoletano. 2018. “Benchmark Analysis of Representative Deep Neural Network Architectures.” IEEE Access 6: 64270–7. https://doi.org/10.1109/access.2018.2877890.

Blalock, Davis, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag. 2020. “What Is the State of Neural Network Pruning?” http://arxiv.org/abs/2003.03033.

Bourely, Alfred, John Patrick Boueri, and Krzysztof Choromonski. 2017. “Sparse Neural Networks Topologies.” http://arxiv.org/abs/1706.05683.

Brown, Tom B, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, et al. 2020. “Language Models Are Few-Shot Learners.” In Advances in Neural Information Processing Systems. http://arxiv.org/abs/2005.14165.

Brutzkus, Alon, Amir Globerson, Eran Malach, and Shai Shalev-Shwartz. 2017. “SGD Learns over-Parameterized Networks That Provably Generalize on Linearly Separable Data.” http://arxiv.org/abs/1710.10174.

Burrascano, P. 1993. “A Pruning Technique Maximizing Generalization.” In Proceedings of 1993 International Conference on Neural Networks (Ijcnn-93-Nagoya, Japan), 1:347–50 vol.1. https://doi.org/10.1109/IJCNN.1993.713928.

Carreira-Perpinan, M. A., and Y. Idelbayev. 2018. “"Learning-Compression" Algorithms for Neural Net Pruning.” In 2018 Ieee/Cvf Conference on Computer Vision and Pattern Recognition, 8532–41. https://doi.org/10.1109/CVPR.2018.00890.

Castellano, G., A. M. Fanelli, and M. Pelillo. 1997. “An Iterative Pruning Algorithm for Feedforward Neural Networks.” IEEE Transactions on Neural Networks 8 (3): 519–31. https://doi.org/10.1109/72.572092.

Castellano, Giovanna, and Anna Maria Fanelli. 2000. “Variable Selection Using Neural-Network Models.” Neurocomputing 31 (1-4): 1–13.

Chandrasekaran, Hema, Hung-Han Chen, and Michael T. Manry. 2000. “Pruning of Basis Functions in Nonlinear Approximators.” Neurocomputing 34 (1): 29–53. https://doi.org/https://doi.org/10.1016/S0925-2312(00)00311-8.

Changpinyo, Soravit, Mark Sandler, and Andrey Zhmoginov. 2017. “The Power of Sparsity in Convolutional Neural Networks.” http://arxiv.org/abs/1702.06257.

Chao, Shih-Kang, Zhanyu Wang, Yue Xing, and Guang Cheng. 2020. “Directional Pruning of Deep Neural Networks.” http://arxiv.org/abs/2006.09358.

Chauvin, Yves. 1989. “A Back-Propagation Algorithm with Optimal Use of Hidden Units.” In Advances in Neural Information Processing Systems 1, 519–26. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.

Chellapilla, Kumar, Sidd Puri, and Patrice Simard. 2006. “High Performance Convolutional Neural Networks for Document Processing.” In.

Chen, Chia-Yu, Jungwook Choi, Daniel Brand, Ankur Agrawal, Wei Zhang, and Kailash Gopalakrishnan. 2017. “AdaComp: Adaptive Residual Gradient Compression for Data-Parallel Distributed Training.” In 32nd Aaai Conference on Artificial Intelligence, 2827–35. http://arxiv.org/abs/1712.02679.

Chen, Jianda, Shangyu Chen, and Sinno Jialin Pan. 2020. “Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning.” Advances in Neural Information Processing Systems 33.

Chen, Tianlong, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, and Michael Carbin. 2020. “The Lottery Ticket Hypothesis for Pre-Trained Bert Networks.” http://arxiv.org/abs/2007.12223.

Chen, Y., T. Krishna, J. S. Emer, and V. Sze. 2017. “Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks.” IEEE Journal of Solid-State Circuits 52 (1): 127–38. https://doi.org/10.1109/JSSC.2016.2616357.

Chen, Yu-Hsin, Tien-Ju Yang, Joel Emer, and Vivienne Sze. 2019. “Eyeriss V2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices.” http://arxiv.org/abs/1807.07928.

Cheng, Yu, Duo Wang, Pan Zhou, and Tao Zhang. 2020. “A Survey of Model Compression and Acceleration for Deep Neural Networks.” http://arxiv.org/abs/1710.09282.

Chérief-Abdellatif, Badr-Eddine. 2019. “Convergence Rates of Variational Inference in Sparse Deep Learning.” http://arxiv.org/abs/1908.04847.

Chetlur, Sharan, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. 2014. “cuDNN: Efficient Primitives for Deep Learning.” http://arxiv.org/abs/1410.0759.

Child, Rewon, Scott Gray, Alec Radford, and Ilya Sutskever. 2019. “Generating Long Sequences with Sparse Transformers.” http://arxiv.org/abs/1904.10509.

Cho, Minsu, Ameya Joshi, and Chinmay Hegde. 2020. “ESPN: Extremely Sparse Pruned Networks.” http://arxiv.org/abs/2006.15741.

Choudhary, Tejalal, Vipul Mishra, Anurag Goswami, and Jagannathan Sarangapani. 2020. “A Comprehensive Survey on Model Compression and Acceleration.” Artificial Intelligence Review, 1–43.

Cibas, Tautvydas, Françoise Fogelman Soulié, Patrick Gallinari, and Sarunas Raudys. 1996. “Variable Selection with Neural Networks.” Neurocomputing 12 (2): 223–48. https://doi.org/https://doi.org/10.1016/0925-2312(95)00121-2.

Cohen, Joseph Paul, Henry Z. Lo, and Wei Ding. 2017. “RandomOut: Using a Convolutional Gradient Norm to Rescue Convolutional Filters.” http://arxiv.org/abs/1602.05931.

Collins, Maxwell D., and Pushmeet Kohli. 2014. “Memory Bounded Deep Convolutional Networks.” CoRR abs/1412.1442. http://arxiv.org/abs/1412.1442.

Correia, Gonçalo M, Vlad Niculae, and André FT Martins. 2019. “Adaptively Sparse Transformers.” In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (Emnlp-Ijcnlp). http://arxiv.org/abs/1909.00015.

Cosentino, Justin, Federico Zaiter, Dan Pei, and Jun Zhu. 2019. “The Search for Sparse, Robust Neural Networks.” http://arxiv.org/abs/1912.02386.

Cui, Baiyun, Yingming Li, Ming Chen, and Zhongfei Zhang. 2019. “Fine-Tune BERT with Sparse Self-Attention Mechanism.” In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (Emnlp-Ijcnlp), 3539–44.

Dai, Bin, Chen Zhu, and David Wipf. 2018. “Compressing Neural Networks Using the Variational Information Bottleneck.” http://arxiv.org/abs/1802.10399.

Dai, Xiaoliang, Hongxu Yin, and Niraj K. Jha. 2018. “NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm.” http://arxiv.org/abs/1711.02017.

d’Ascoli, Stéphane, Levent Sagun, Joan Bruna, and Giulio Biroli. 2020. “Finding the Needle in the Haystack with Convolutions: On the Benefits of Architectural Bias.” http://arxiv.org/abs/1906.06766.

Dave, Shail, Riyadh Baghdadi, Tony Nowatzki, Sasikanth Avancha, Aviral Shrivastava, and Baoxin Li. 2020. “Hardware Acceleration of Sparse and Irregular Tensor Computations of Ml Models: A Survey and Insights.” http://arxiv.org/abs/2007.00864.

Davies, Peter, Vijaykrishna Gurunathan, Niusha Moshrefi, Saleh Ashkboos, and Dan Alistarh. 2020. “Distributed Variance Reduction with Optimal Communication.” http://arxiv.org/abs/2002.09268.

Deng, L., G. Li, S. Han, L. Shi, and Y. Xie. 2020. “Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey.” Proceedings of the IEEE 108 (4): 485–532. https://doi.org/10.1109/JPROC.2020.2976475.

Denil, Misha, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, and Nando de Freitas. 2014. “Predicting Parameters in Deep Learning.” http://arxiv.org/abs/1306.0543.

Denton, Emily L, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus. 2014. “Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation.” Advances in Neural Information Processing Systems 27: 1269–77.

Dettmers, Tim, and Luke Zettlemoyer. 2019. “Sparse Networks from Scratch: Faster Training Without Losing Performance.” http://arxiv.org/abs/1907.04840.

De Vivo, Luisa, Michele Bellesi, William Marshall, Eric A Bushong, Mark H Ellisman, Giulio Tononi, and Chiara Cirelli. 2017. “Ultrastructural Evidence for Synaptic Scaling Across the Wake/Sleep Cycle.” Science 355 (6324): 507–10.

Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–86.

Dey, S., K. Huang, P. A. Beerel, and K. M. Chugg. 2019. “Pre-Defined Sparse Neural Networks with Hardware Acceleration.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems 9 (2): 332–45. https://doi.org/10.1109/JETCAS.2019.2910864.

Diering, Graham H, Raja S Nirujogi, Richard H Roth, Paul F Worley, Akhilesh Pandey, and Richard L Huganir. 2017. “Homer1a Drives Homeostatic Scaling-down of Excitatory Synapses During Sleep.” Science 355 (6324): 511–15.

Ding, Xiaohan, Guiguang Ding, Yuchen Guo, and Jungong Han. 2019. “Centripetal Sgd for Pruning Very Deep Convolutional Networks with Complicated Structure.” http://arxiv.org/abs/1904.03837.

Ding, Xiaohan, Guiguang Ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, and Ji Liu. 2019. “Global Sparse Momentum Sgd for Pruning Very Deep Neural Networks.” http://arxiv.org/abs/1909.12778.

Dolan, William B, and Chris Brockett. 2005. “Automatically Constructing a Corpus of Sentential Paraphrases.” In Proceedings of the Third International Workshop on Paraphrasing (Iwp2005).

Domingos, Pedro. 2020. “Every Model Learned by Gradient Descent Is Approximately a Kernel Machine.” http://arxiv.org/abs/2012.00152.

Dong, Xiao, Lei Liu, Guangli Li, Jiansong Li, Peng Zhao, Xueying Wang, and Xiaobing Feng. 2019. “Exploiting the Input Sparsity to Accelerate Deep Neural Networks: Poster.” In Proceedings of the 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Ppopp 2019, Washington, Dc, Usa, February 16-20, 2019, 401–2. https://doi.org/10.1145/3293883.3295713.

Dong, Xin, Shangyu Chen, and Sinno Jialin Pan. 2017. “Learning to Prune Deep Neural Networks via Layer-Wise Optimal Brain Surgeon.” http://arxiv.org/abs/1705.07565.

Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, et al. 2021. “An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale.” In Proceedings of the Ninth International Conference on Learning Representations. http://arxiv.org/abs/2010.11929.

Dryden, Nikoli, Tim Moon, Sam Ade Jacobs, and Brian Van Essen. 2016. “Communication Quantization for Data-Parallel Training of Deep Neural Networks.” In 2nd Workshop on Machine Learning in Hpc Environments (Mlhpc), 1–8.

Du, Simon S., Xiyu Zhai, Barnabas Poczos, and Aarti Singh. 2019. “Gradient Descent Provably Optimizes over-Parameterized Neural Networks.” http://arxiv.org/abs/1810.02054.

Dutta, Aritra, El Houcine Bergou, Ahmed M Abdelmoniem, Chen-Yu Ho, Atal Narayan Sahu, Marco Canini, and Panos Kalnis. 2020. “On the Discrepancy Between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning.” In Proceedings of the Aaai Conference on Artificial Intelligence, 34:3817–24. 04. http://arxiv.org/abs/1911.08250.

Elsen, Erich, Marat Dukhan, Trevor Gale, and Karen Simonyan. 2019. “Fast Sparse Convnets.” http://arxiv.org/abs/1911.09723.

Elsken, Thomas, Jan Hendrik Metzen, and Frank Hutter. 2019. “Neural Architecture Search: A Survey.” http://arxiv.org/abs/1808.05377.

Engelbrecht, Andries Petrus, Ian Cloete, and Jacek M Zurada. 1995. “Determining the Significance of Input Parameters Using Sensitivity Analysis.” In International Workshop on Artificial Neural Networks, 382–88. Springer.

Engelbrecht, A. P. 2001. “A New Pruning Heuristic Based on Variance Analysis of Sensitivity Information.” IEEE Transactions on Neural Networks 12 (6): 1386–99. https://doi.org/10.1109/72.963775.

Engelbrecht, A. P., and I. Cloete. 1996. “A Sensitivity Analysis Algorithm for Pruning Feedforward Neural Networks.” In Proceedings of International Conference on Neural Networks (Icnn’96), 2:1274–8 vol.2. https://doi.org/10.1109/ICNN.1996.549081.

Evci, Utku, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen. 2020. “Rigging the Lottery: Making All Tickets Winners.” http://arxiv.org/abs/1911.11134.

Evci, Utku, Yani A. Ioannou, Cem Keskin, and Yann Dauphin. 2020. “Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win.” http://arxiv.org/abs/2010.03533.

Evci, Utku, Fabian Pedregosa, Aidan Gomez, and Erich Elsen. 2020. “The Difficulty of Training Sparse Neural Networks.” http://arxiv.org/abs/1906.10732.

Fan, Angela, Edouard Grave, and Armand Joulin. 2020. “Reducing Transformer Depth on Demand with Structured Dropout.” In Proceedings of the Eighth International Conference on Learning Representations. http://arxiv.org/abs/1909.11556.

Fedus, William, Barret Zoph, and Noam Shazeer. 2021. “Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity.” http://arxiv.org/abs/2101.03961.

Finnoff, William, Ferdinand Hergert, and Hans Georg Zimmermann. 1993. “Improving Model Selection by Nonconvergent Methods.” Neural Networks 6 (6): 771–83.

Fletcher, L., V. Katkovnik, F. E. Steffens, and A. P. Engelbrecht. 1998. “Optimizing the Number of Hidden Nodes of a Feedforward Artificial Neural Network.” In 1998 Ieee International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227), 2:1608–12 vol.2. https://doi.org/10.1109/IJCNN.1998.686018.

Frankle, Jonathan, and Michael Carbin. 2019. “The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.” http://arxiv.org/abs/1803.03635.

Frankle, Jonathan, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin. 2020a. “Linear Mode Connectivity and the Lottery Ticket Hypothesis.” http://arxiv.org/abs/1912.05671.

———. 2020b. “Stabilizing the Lottery Ticket Hypothesis.” http://arxiv.org/abs/1903.01611.

———. 2021. “Pruning Neural Networks at Initialization: Why Are We Missing the Mark?” http://arxiv.org/abs/2009.08576.

Frankle, Jonathan, David J. Schwab, and Ari S. Morcos. 2020. “The Early Phase of Neural Network Training.” http://arxiv.org/abs/2002.10365.

Friedman, J., T. Hastie, and R. Tibshirani. 2010. “A Note on the Group Lasso and a Sparse Group Lasso.” http://arxiv.org/abs/1001.0736.

Friston, K.J. 2008. “Hierarchical Models in the Brain.” PLOS Computational Biology 4 (11): e1000211. https://doi.org/10.1371/journal.pcbi.1000211.

Gaier, Adam, and David Ha. 2019. “Weight Agnostic Neural Networks.” http://arxiv.org/abs/1906.04358.

Gal, Yarin, and Zoubin Ghahramani. 2016. “Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.” In Proceedings of the 33rd International Conference on Machine Learning, edited by Maria Florina Balcan and Kilian Q. Weinberger, 48:1050–9. Proceedings of Machine Learning Research. New York, New York, USA: PMLR. http://proceedings.mlr.press/v48/gal16.html.

Gal, Yarin, Jiri Hron, and Alex Kendall. 2017. “Concrete Dropout.” In Advances in Neural Information Processing Systems, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 30:3581–90. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/file/84ddfb34126fc3a48ee38d7044e87276-Paper.pdf.

Gale, Trevor, Erich Elsen, and Sara Hooker. 2019. “The State of Sparsity in Deep Neural Networks.” http://arxiv.org/abs/1902.09574.

Gale, Trevor, Matei Zaharia, Cliff Young, and Erich Elsen. 2020. “Sparse Gpu Kernels for Deep Learning.” http://arxiv.org/abs/2006.10901.

Ganesh, Prakhar, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Deming Chen, Marianne Winslett, Hassan Sajjad, and Preslav Nakov. 2020. “Compressing Large-Scale Transformer-Based Models: A Case Study on BERT.” http://arxiv.org/abs/2002.11985.

Ge, Dongdong, Xiaoye Jiang, and Yinyu Ye. 2011. “A Note on the Complexity of L P Minimization.” Mathematical Programming 129 (2): 285–99.

Georgiadis, Georgios. 2019. “Accelerating Convolutional Neural Networks via Activation Map Compression.” In Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, 7085–95.

Ghiasi, Golnaz, Tsung-Yi Lin, and Quoc V Le. 2018. “DropBlock: A Regularization Method for Convolutional Networks.” In Advances in Neural Information Processing Systems, edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, 31:10727–37. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2018/file/7edcfb2d8f6a659ef4cd1e6c9b6d7079-Paper.pdf.

Ghosh, Joydeep, and Kagan Tumer. 1994. “Structural Adaptation and Generalization in Supervised Feed-Forward Networks.” J. Artif. Neural Netw. 1 (4): 431–58.

Glorot, Xavier, and Yoshua Bengio. 2010. “Understanding the Difficulty of Training Deep Feedforward Neural Networks.” In AISTATS, edited by Yee Whye Teh and D. Mike Titterington, 9:249–56. JMLR Proceedings. JMLR.org. http://dblp.uni-trier.de/db/journals/jmlr/jmlrp9.html#GlorotB10.

Glorot, Xavier, Antoine Bordes, and Yoshua Bengio. 2011a. “Deep Sparse Rectifier Neural Networks.” In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 315–23.

———. 2011b. “Deep Sparse Rectifier Neural Networks.” In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 315–23.

Golub, Maximilian, Guy Lemieux, and Mieszko Lis. 2019. “Full Deep Neural Network Training on a Pruned Weight Budget.” http://arxiv.org/abs/1806.06949.

Gomez, Aidan N., Ivan Zhang, Siddhartha Rao Kamalakara, Divyam Madaan, Kevin Swersky, Yarin Gal, and Geoffrey E. Hinton. 2019. “Learning Sparse Networks Using Targeted Dropout.” http://arxiv.org/abs/1905.13678.

Gondimalla, Ashish, Noah Chesnut, Mithuna Thottethodi, and T. N. Vijaykumar. 2019. “SparTen: A Sparse Tensor Accelerator for Convolutional Neural Networks.” In Proceedings of the 52nd Annual Ieee/Acm International Symposium on Microarchitecture, 151–65. MICRO ’52. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3352460.3358291.

Goodfellow, Ian J., Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Networks.” http://arxiv.org/abs/1406.2661.

Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. “Generative Adversarial Nets.” In Advances in Neural Information Processing Systems, 2672–80. http://arxiv.org/abs/1406.2661.

Gopalakrishnan, Soorya, Zhinus Marzi, Upamanyu Madhow, and Ramtin Pedarsani. 2018. “Combating Adversarial Attacks Using Sparse Representations.” http://arxiv.org/abs/1803.03880.

Gordon, Ariel, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, and Edward Choi. 2018. “Morphnet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks.” In Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, 1586–95.

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