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The rapid proliferation of machine learning models across domains and deployment settings has given rise to various communities (e.g.
Long short-term memory
Jürgen Schmidhuber and Sepp Hochreiter · 1997
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts · 2013
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Humans require context to infer ironic intent (so computers probably do, too)
Byron C. Wallace, Do Kook Choe, Laura Kertz, and Eugene Charniak · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation, 2014
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Convolutional neural networks for sentence classification, 2014
Yoon Kim · 2014
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Imagenet large scale visual recognition challenge, 2015
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
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Very deep convolutional networks for large-scale image recognition, 2015
Karen Simonyan and Andrew Zisserman · 2015
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Squad: 100,000+ questions for machine comprehension of text, 2016
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Character-level convolutional networks for text classification, 2016
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2016
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Psutil package: a cross-platform library for retrieving information on running processes and system utilization
Giampaolo Rodola · 2016
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Identity mappings in deep residual networks, 2016
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Dawnbench: An end-to-end deep learning benchmark and competition
Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Chris Ré, and Matei Zaharia · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
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Automated hate speech detection and the problem of offensive language
Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Attention is all you need, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Models matter, so does training: An empirical study of cnns for optical flow estimation, 2018
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz · 2018
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On the importance of strong baselines in bayesian deep learning
Jishnu Mukhoti, Pontus Stenetorp, and Yarin Gal · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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scikit-optimize/scikit-optimize: v0.5.2, mar 2018
Tim Head, MechCoder, Gilles Louppe, Iaroslav Shcherbatyi, fcharras, Zé Vinícius, cmmalone, Christopher Schröder, nel215, Nuno Campos, Todd Young, Stefano Cereda, Thomas Fan, rene rex, Kejia (KJ) Shi, Justus Schwabedal, carlosdanielcsantos, Hvass-Labs, Mikhail Pak, SoManyUsernamesTaken, Fred Callaway, Loïc Estève, Lilian Besson, Mehdi Cherti, Karlson Pfannschmidt, Fabian Linzberger, Christophe Cauet, Anna Gut, Andreas Mueller, and Alexander Fabisch · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
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Black-box generation of adversarial text sequences to evade deep learning classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa, and Yanjun Qi · 2018
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Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber · 2018
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Data statements for natural language processing: Toward mitigating system bias and enabling better science
Emily M Bender and Batya Friedman · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Tune: A research platform for distributed model selection and training
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding, 2019
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 2019
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How the transformers broke nlp leaderboards, Jun 2019
Anna Rogers · 2019
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Shinylearner: A containerized benchmarking tool for machine-learning classification of tabular data
Stephen R Piccolo, Terry J Lee, Erica Suh, and Kimball Hill · 2020
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Demoting racial bias in hate speech detection
Mengzhou Xia, Anjalie Field, and Yulia Tsvetkov · 2020
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Multi-dimensional gender bias classification, 2020
Emily Dinan, Angela Fan, Ledell Wu, Jason Weston, Douwe Kiela, and Adina Williams · 2020
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Goemotions: A dataset of fine-grained emotions, 2020
Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, and Sujith Ravi · 2020
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Social bias frames: Reasoning about social and power implications of language, 2020
Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi · 2020
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Electra: Pre-training text encoders as discriminators rather than generators
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Piero Molino, Yaroslav Dudin, and Sai Sumanth Miryala · 2019
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Text classification algorithms: A survey
Kowsari, Jafari Meimandi, Heidarysafa, Mendu, Barnes, and Brown · 2019
Cited alongside, same era.
How to fine-tune bert for text classification?
Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang · 2019
Cited alongside, same era.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Cited alongside, same era.
Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
Cited alongside, same era.
Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
Cited alongside, same era.
Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christopher D Manning · 2020
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mt5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel · 2020
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A comparison of lstm and bert for small corpus, 2020
Aysu Ezen-Can · 2020
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On adversarial bias and the robustness of fair machine learning
Hongyan Chang, Ta Duy Nguyen, Sasi Kumar Murakonda, Ehsan Kazemi, and Reza Shokri · 2020
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Language (technology) is power: A critical survey of" bias" in nlp
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach · 2020
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Tabnet: Attentive interpretable tabular learning, 2020
Sercan O. Arik and Tomas Pfister · 2020
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Xlnet: Generalized autoregressive pretraining for language understanding, 2020
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le · 2020
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Flaubert: Unsupervised language model pre-training for french, 2020
Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, and Didier Schwab · 2020
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Camembert: a tasty french language model
Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Djamé Seddah, and Benoît Sagot · 2020
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Unsupervised cross-lingual representation learning at scale, 2020
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov · 2020
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Longformer: The long-document transformer, 2020
Iz Beltagy, Matthew E. Peters, and Arman Cohan · 2020
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Descending through a crowded valley – benchmarking deep learning optimizers, 2021
Robin M. Schmidt, Frank Schneider, and Philipp Hennig · 2021
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Robustness gym: Unifying the nlp evaluation landscape
Karan Goel, Nazneen Rajani, Jesse Vig, Samson Tan, Jason Wu, Stephan Zheng, Caiming Xiong, Mohit Bansal, and Christopher Ré · 2021
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Explainaboard: An explainable leaderboard for nlp
Pengfei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaicheng Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, and Graham Neubig · 2021
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Dynaboard: Moving beyond accuracy to holistic model evaluation in nlp
Douwe Kiela, Zhiyi Ma, Tristan Thrush, Somya Jain, Ledell Wu, Robin Jia, and Adina Williams · 2021
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Deep learning based text classification: A comprehensive review, 2021
Shervin Minaee, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao · 2021
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Jack Bandy and Nicholas Vincent · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
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Mlp-mixer: An all-mlp architecture for vision, 2021
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
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 · 2021
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