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Deep Neural Networks (DNNs) are becoming integral components of real world services relied upon by millions of users.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Deep ensembles: A loss landscape perspective
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan. 2019 · 1912
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020 · 2002
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How good is the bayes posterior in deep neural networks really?
Florian Wenzel, Kevin Roth, Bastiaan S Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, and Sebastian Nowozin. 2020 · 2002
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Dialoglue: A natural language understanding benchmark for task-oriented dialogue
Shikib Mehri, Mihail Eric, and Dilek Z. Hakkani-Tür. 2020 · 2009
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi. 2020 · 2009
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2014 · 2014
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Launch and iterate: Reducing prediction churn
Mahdi Milani Fard, Quentin Cormier, Kevin Canini, and Maya Gupta. 2016 · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
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Born again neural networks
Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar. 2018 · 2018
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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, T. Garipov, Dmitry P. Vetrov, and Andrew Gordon Wilson. 2018 · 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 · 2018
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Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals
Andrew Cotter, Heinrich Jiang, Maya R Gupta, Serena Wang, Taman Narayan, Seungil You, and Karthik Sridharan. 2019 · 2019
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Show your work: Improved reporting of experimental results
Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, and Noah A. Smith. 2019 · 2019
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Locally adaptive label smoothing for predictive churn
Dara Bahri and Heinrich Jiang. 2021 · 2021
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On the reproducibility of neural network predictions
Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Menon, and Sanjiv Kumar. 2021 · 2021
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What are bayesian neural network posteriors really like?
Pavel Izmailov, Sharad Vikram, Matthew D Hoffman, and Andrew Gordon Gordon Wilson. 2021 · 2021
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Churn reduction via distillation
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, and Afshin Rostamizadeh. 2021 · 2021
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Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning
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An evaluation dataset for intent classification and out-of-scope prediction
Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, and Jason Mars. 2019 · 2019
Cited alongside, same era.
A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson. 2019 · 2019
Cited alongside, same era.
When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton. 2019 · 2019
Cited alongside, same era.
Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al. 2020a
Cited in the paper.
Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al. 2020b
Cited in the paper.
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W Dusenberry, Sebastian Farquhar, Qixuan Feng, Angelos Filos, Marton Havasi, Rodolphe Jenatton, et al. 2021 · 2021
Later among the works it cites.
Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, and D. Sculley. 2022 · 2022
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Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, et al. 2022 · 2022
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Reducing model jitter: Stable re-training of semantic parsers in production environments
Christopher Hidey, Fei Liu, and Rahul Goel. 2022 · 2022
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