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There has been significant research done on developing methods for improving robustness to distributional shift and uncertainty estimation.
“Contributions to the Theory of Statistical Estimation and Testing Hypotheses,”
Abraham Wald, · 1939
Earlier work this paper cites.
“Gradient-based learning applied to document recognition,”
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, · 1998
Earlier work this paper cites.
“The problem of concept drift: definitions and related work,”
Alexey Tsymbal, · 2004
Earlier work this paper cites.
Dataset Shift in Machine Learning
Joaquin Quiñonero-Candela, · 2009
Earlier work this paper cites.
“ImageNet: A Large-Scale Hierarchical Image Database,”
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, · 2009
Earlier work this paper cites.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky, · 2009
Earlier work this paper cites.
“Recurrent Neural Network Based Language Model,”
Tomas Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, and Sanjeev Khudanpur, · 2010
Earlier work this paper cites.
“Deep neural networks for acoustic modeling in speech recognition,”
Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, and Brian Kingsbury, · 2012
Earlier work this paper cites.
“Linguistic Regularities in Continuous Space Word Representations,”
Tomas Mikolov et al., · 2013
Earlier work this paper cites.
Ian J. Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, and Vinay D. Shet, · 2013
Earlier work this paper cites.
“Using the köppen classification to quantify climate variation and change: An example for 1901–2010,”
Deliang Chen and Hans Weiteng Chen, · 2013
Earlier work this paper cites.
“A survey on concept drift adaptation,”
João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia, · 2014
Earlier work this paper cites.
“Learning phrase representations using rnn encoder-decoder for statistical machine translation,”
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio, · 2014
Earlier work this paper cites.
“Very Deep Convolutional Networks for Large-Scale Image Recognition,”
Karen Simonyan and Andrew Zisserman, · 2015
Earlier work this paper cites.
“Neural machine translation by jointly learning to align and translate,”
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio, · 2015
Earlier work this paper cites.
“Human-level concept learning through probabilistic program induction,”
Brenden M. Lake, Ruslan Salakhutdinov, and Joshua B. Tenenbaum, · 2015
Earlier work this paper cites.
“Ground truth for grammatical error correction metrics,”
Courtney Napoles, Keisuke Sakaguchi, Matt Post, and Joel Tetreault, · 2015
Earlier work this paper cites.
“Concrete problems in AI safety,” http://arxiv.org/abs/1606.06565 , 2016,
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul F. Christiano, John Schulman, and Dan Mané, · 2016
Earlier work this paper cites.
Uncertainty in Deep Learning
Yarin Gal, · 2016
Earlier work this paper cites.
“Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning,”
Yarin Gal and Zoubin Ghahramani, · 2016
Earlier work this paper cites.
Courtney Napoles, Keisuke Sakaguchi, Matt Post, and Joel Tetreault, · 2016
Earlier work this paper cites.
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al., · 2016
Earlier work this paper cites.
“Social lstm: Human trajectory prediction in crowded spaces,”
Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Silvio Savarese, · 2016
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Variational inference with normalizing flows,” 2016
Danilo Jimenez Rezende and Shakir Mohamed, · 2016
Earlier work this paper cites.
“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin, · 2017
Earlier work this paper cites.
“Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles,”
B. Lakshminarayanan, A. Pritzel, and C. Blundell, · 2017
Earlier work this paper cites.
“Adversarial examples are not easily detected: Bypassing ten detection methods,”
Nicholas Carlini and David A. Wagner, · 2017
Earlier work this paper cites.
“Desire: Distant future prediction in dynamic scenes with interacting agents,”
Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B Choy, Philip HS Torr, and Manmohan Chandraker, · 2017
Cited alongside, same era.
“Understanding Measures of Uncertainty for Adversarial Example Detection,”
L. Smith and Y. Gal, · 2018
Cited alongside, same era.
“Predictive uncertainty estimation via prior networks,”
Andrey Malinin and Mark Gales, · 2018
Cited alongside, same era.
“MTNT: A testbed for Machine Translation of Noisy Text,”
Paul Michel and Graham Neubig, · 2018
Cited alongside, same era.
“Catboost: unbiased boosting with categorical features,”
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin, · 2018
Cited alongside, same era.
“A call for clarity in reporting BLEU scores,”
Matt Post, · 2018
Cited alongside, same era.
“Unsupervised quality estimation for neural machine translation,”
Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia, · 2020
Later among the works it cites.
“Enhancing the reliability of out-of-distribution image detection in neural networks,” 2020
Shiyu Liang, Yixuan Li, and R. Srikant, · 2020
Later among the works it cites.
“Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data,” 2020
Yen-Chang Hsu, Yilin Shen, Hongxia Jin, and Zsolt Kira, · 2020
Later among the works it cites.
“Uncertainty estimation using a single deep deterministic neural network,”
Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal, · 2020
Later among the works it cites.
“Training independent subnetworks for robust prediction,” 2020
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, and Dustin Tran, · 2020
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“Social gan: Socially acceptable trajectories with generative adversarial networks,”
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi, · 2018
Cited alongside, same era.
“End-to-end driving via conditional imitation learning,”
Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy, · 2018
Cited alongside, same era.
“Deep imitative models for flexible inference, planning, and control,”
Nicholas Rhinehart, Rowan McAllister, and Sergey Levine, · 2018
Cited alongside, same era.
“Understanding measures of uncertainty for adversarial example detection,” 2018
Lewis Smith and Yarin Gal, · 2018
Cited alongside, same era.
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford, · 2018
Cited alongside, same era.
Uncertainty Estimation in Deep Learning with application to Spoken Language Assessment
Andrey Malinin, · 2019
Cited alongside, same era.
Later among the works it cites.
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan, · 2020
Later among the works it cites.
“Regression prior networks,” 2020
Andrey Malinin, Sergey Chervontsev, Ivan Provilkov, and Mark Gales, · 2020
Later among the works it cites.
“Ensemble distribution distillation,”
Andrey Malinin, Bruno Mlodozeniec, and Mark JF Gales, · 2020
Later among the works it cites.
“The many faces of robustness: A critical analysis of out-of-distribution generalization,” 2020
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer, · 2020
Later among the works it cites.
“Covernet: Multimodal behavior prediction using trajectory sets,”
Tung Phan-Minh, Elena Corina Grigore, Freddy A Boulton, Oscar Beijbom, and Eric M Wolff, · 2020
Later among the works it cites.
“Vectornet: Encoding hd maps and agent dynamics from vectorized representation,”
Jiyang Gao, Chen Sun, Hang Zhao, Yi Shen, Dragomir Anguelov, Congcong Li, and Cordelia Schmid, · 2020
Later among the works it cites.
“Prank: motion prediction based on ranking,”
Yuriy Biktairov, Maxim Stebelev, Irina Rudenko, Oleh Shliazhko, and Boris Yangel, · 2020
Later among the works it cites.
“Implicit latent variable model for scene-consistent motion forecasting,”
Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, and Raquel Urtasun, · 2020
Later among the works it cites.
“Learning lane graph representations for motion forecasting,”
Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, and Raquel Urtasun, · 2020
Later among the works it cites.
“Can autonomous vehicles identify, recover from, and adapt to distribution shifts?,”
Angelos Filos, Panagiotis Tigkas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, and Yarin Gal, · 2020
Later among the works it cites.
“Deep ensembles: A loss landscape perspective,” 2020
Stanislav Fort, Huiyi Hu, and Balaji Lakshminarayanan, · 2020
Later among the works it cites.
“Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning,” 2021
Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, and Dustin Tran, · 2021
Closest in time.
“Uncertainty estimation in autoregressive structured prediction,”
Andrey Malinin and Mark Gales, · 2021
Closest in time.
“Improving deterministic uncertainty estimation in deep learning for classification and regression,”
Joost van Amersfoort, Lewis Smith, Andrew Jesson, Oscar Key, and Yarin Gal, · 2021
Closest in time.
Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip HS Torr, and Yarin Gal, · 2021
Closest in time.
“Scaling ensemble distribution distillation to many classes with proxy targets,”
Max Ryabinin, Andrey Malinin, and Mark Gales, · 2021
Closest in time.
“Benchmarking bayesian deep learning on diabetic retinopathy detection tasks,”
Neil Band, Tim G. J. Rudner, Qixuan Feng, Angelos Filos, Zachary Nado, Michael W. Dusenberry, Ghassen Jerfel, Dustin Tran, and Yarin Gal, · 2021
Closest in time.
“Natural adversarial examples,” 2021
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song, · 2021
Closest in time.
“Uncertainty in gradient boosting via ensembles,”
Andrey Malinin, Liudmila Prokhorenkova, and Aleksei Ustimenko, · 2021
Closest in time.
“Multimodal motion prediction with stacked transformers,”
Yicheng Liu, Jinghuai Zhang, Liangji Fang, Qinhong Jiang, and Bolei Zhou, · 2021
Closest in time.
“Revisiting deep learning models for tabular data,”
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko, · 2021
Closest in time.
“Multimodal trajectory predictions for autonomous driving using deep convolutional networks,”
Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou, Tsung-Han Lin, Thi Nguyen, Tzu-Kuo Huang, Jeff Schneider, and Nemanja Djuric, · 2096
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