SlowGuess/ACL_2020_OCR huggingface dataset:
Jonathan H. Clark, Chris Dyer, Alon Lavie, and Noah A. Smith. 2011. Better hypothesis testing for statistical machine translation: Controlling for optimizer instability. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 176-181, Portland, Oregon, USA. Association for Computational Linguistics.
Maxwell D. Collins and Pushmeet Kohli. 2014. Memory bounded deep convolutional networks. CoRR, abs/1412.1442.
Yann N. Dauphin and Yoshua Bengio. 2013. Big neural networks waste capacity. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings.
Misha Denil, Babak Shakibi, Laurent Dinh, MarcAurelio Ranzato, and Nando de Freitas. 2013. Predicting parameters in deep learning. In Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, NIPS13, page 21482156, Red Hook, NY, USA. Curran Associates Inc.
Erich Elsen, Marat Dukhan, Trevor Gale, and Karen Simonyan. 2019. Fast sparse convnets.
Jonathan Frankle and Michael Carbin. 2018. The lottery ticket hypothesis: Training pruned neural networks. CoRR, abs/1803.03635.
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin. 2019. The lottery ticket hypothesis at scale. CoRR, abs/1903.01611.
Jonathan Frankle, David J. Schwab, and Ari S. Morcos. 2020. The early phase of neural network training. In International Conference on Learning Representations.
Trevor Gale, Erich Elsen, and Sara Hooker. 2019. The state of sparsity in deep neural networks. CoRR, abs/1902.09574.
Yiwen Guo, Anbang Yao, and Yurong Chen. 2016. Dynamic network surgery for efficient dnns. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, pages 1379-1387. Curran Associates, Inc.
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and William J. Dally. 2016. EIE: efficient inference engine on compressed deep neural network. CoRR, abs/1602.01528.
Song Han, Jeff Pool, John Tran, and William Dally. 2015. Learning both weights and connections for efficient neural network. In C. Cortes, N. D.
Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 1135-1143. Curran Associates, Inc.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778.
Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky. 2015. Sparse convolutional neural networks. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Sharan Narang, Greg Diamos, Shubho Sengupta, and Erich Elsen. 2017. Exploring sparsity in recurrent neural networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net.
Matt Post. 2018. A call for clarity in reporting BLEU scores. In Proceedings of the Third Conference on Machine Translation: Research Papers, pages 186-191, Belgium, Brussels. Association for Computational Linguistics.
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. 2015. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211-252.
Ivan Stanimirovi and Milan Tasic. 2009. Performance comparison of storage formats for sparse matrices. Facta Universitatis. Series Mathematics and Informatics, 24.
Ashish Vaswani, Samy Bengio, Eugene Brevdo, Francois Chollet, Aidan Gomez, Stephan Gouws, Llion Jones, Lukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, and Jakob Uszkoreit. 2018. Tensor2Tensor for neural machine translation. In Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Papers), pages 193-199, Boston, MA. Association for Machine Translation in the Americas.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 5998-6008. Curran Associates, Inc.
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren
Weckesser, Jonathan Bright, Stefan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, Ilhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R Harris, Anne M. Archibald, Antonio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. 2020. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods.
Elena Voita, David Talbot, Fedor Moiseev, Rico Senrich, and Ivan Titov. 2019. Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5797-5808, Florence, Italy. Association for Computational Linguistics.
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyls, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. CoRR, abs/1609.08144.
Haonan Yu, Sergey Edunov, Yuandong Tian, and Ari S. Morcos. 2020. Playing the lottery with rewards and multiple languages: lottery tickets in rl and nlp. In International Conference on Learning Representations.
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski. 2019. Deconstructing lottery tickets: Zeros, signs, and the supermask. In H. Wallach, H. Larochelle, A. Beygelzimer, F. dAlché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 3597-3607. Curran Associates, Inc.
Michael Zhu and Suyog Gupta. 2018. To prune, or not to prune: Exploring the efficacy of pruning for model compression. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Workshop Track Proceedings. OpenReview.net.