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Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions. In ICML .  AM Alaa and M van der Schaar. 2020. Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions. In ICML ."},{"key":"e_1_3_2_2_3_1","volume-title":"Concrete problems in AI safety. arXiv preprint arXiv:1606.06565","author":"Amodei Dario","year":"2016","unstructured":"Dario Amodei , Chris Olah , Jacob Steinhardt , Paul Christiano , John Schulman , and Dan Man\u00e9 . 2016. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565 ( 2016 ). Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Man\u00e9. 2016. Concrete problems in AI safety. arXiv preprint arXiv:1606.06565 (2016)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/jtsa.12426"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0906910106"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2010.07.002"},{"key":"e_1_3_2_2_7_1","unstructured":"Charles Blundell Julien Cornebise Koray Kavukcuoglu and Daan Wierstra. 2015. Weight Uncertainty in Neural Network. In ICML. 1613--1622.  Charles Blundell Julien Cornebise Koray Kavukcuoglu and Daan Wierstra. 2015. Weight Uncertainty in Neural Network. In ICML. 1613--1622."},{"key":"e_1_3_2_2_8_1","volume-title":"Verification of forecasts expressed in terms of probability. Monthly weather review","author":"Brier Glenn W","year":"1950","unstructured":"Glenn W Brier . 1950. Verification of forecasts expressed in terms of probability. Monthly weather review , Vol. 78 , 1 ( 1950 ), 1--3. Glenn W Brier. 1950. Verification of forecasts expressed in terms of probability. Monthly weather review , Vol. 78, 1 (1950), 1--3."},{"key":"e_1_3_2_2_9_1","volume-title":"Kunpeng Mu, Luca Rossi, Kaiyuan Sun, C\u00e9cile Viboud, Xinyue Xiong, Hongjie Yu, M. Elizabeth Halloran, Ira M. Longini, and Alessandro Vespignani.","author":"Chinazzi Matteo","year":"2020","unstructured":"Matteo Chinazzi , Jessica T. Davis , Marco Ajelli , Corrado Gioannini , Maria Litvinova , Stefano Merler , Ana Pastore y Piontti , Kunpeng Mu, Luca Rossi, Kaiyuan Sun, C\u00e9cile Viboud, Xinyue Xiong, Hongjie Yu, M. Elizabeth Halloran, Ira M. Longini, and Alessandro Vespignani. 2020 . The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science , Vol. 368 , 6489 (2020), 395--400. Matteo Chinazzi, Jessica T. Davis, Marco Ajelli, Corrado Gioannini, Maria Litvinova, Stefano Merler, Ana Pastore y Piontti, Kunpeng Mu, Luca Rossi, Kaiyuan Sun, C\u00e9cile Viboud, Xinyue Xiong, Hongjie Yu, M. 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An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases , Vol. 20, 5 (2020), 533--534."},{"key":"e_1_3_2_2_14_1","unstructured":"M. Dusenberry G. Jerfel Y. Wen Y.-A. Ma J. Snoek K. Heller B. Lakshminarayanan and D. Tran. 2020. Efficient and scalable Bayesian neural nets with rank-1 factors. In ICML. 9823--9833.  M. Dusenberry G. Jerfel Y. Wen Y.-A. Ma J. Snoek K. Heller B. Lakshminarayanan and D. Tran. 2020. Efficient and scalable Bayesian neural nets with rank-1 factors. In ICML. 9823--9833."},{"key":"e_1_3_2_2_15_1","volume-title":"Computer Age Statistical Inference","author":"Efron Bradley","unstructured":"Bradley Efron and Trevor Hastie . 2016. Computer Age Statistical Inference . Vol. 5 . Cambridge University Press . Bradley Efron and Trevor Hastie. 2016. Computer Age Statistical Inference . Vol. 5. Cambridge University Press."},{"key":"e_1_3_2_2_16_1","unstructured":"S. Fort H. Hu and B. Lakshminarayanan. 2019. Deep ensembles: A loss landscape perspective. (2019). arXiv:1912.02757.  S. Fort H. Hu and B. Lakshminarayanan. 2019. Deep ensembles: A loss landscape perspective. (2019). arXiv:1912.02757."},{"key":"e_1_3_2_2_17_1","unstructured":"Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In ICML . 1050--1059.  Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In ICML . 1050--1059."},{"key":"e_1_3_2_2_18_1","unstructured":"Yarin Gal Jiri Hron and Alex Kendall. 2017. Concrete dropout. In NIPS. 3581--3590.  Yarin Gal Jiri Hron and Alex Kendall. 2017. Concrete dropout. In NIPS. 3581--3590."},{"key":"e_1_3_2_2_19_1","volume-title":"David Salinas, Valentin Flunkert, and Tim Januschowski.","author":"Gasthaus Jan","year":"2019","unstructured":"Jan Gasthaus , Konstantinos Benidis , Yuyang Wang , Syama Sundar Rangapuram , David Salinas, Valentin Flunkert, and Tim Januschowski. 2019 . Probabilistic forecasting with spline quantile function RNNs. In AISTATS 22 . 1901--1910. Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski. 2019. Probabilistic forecasting with spline quantile function RNNs. In AISTATS 22 . 1901--1910."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000001437"},{"key":"e_1_3_2_2_22_1","unstructured":"Alex Graves. 2011. Practical variational inference for neural networks. In NIPS. 2348--2356.  Alex Graves. 2011. Practical variational inference for neural networks. In NIPS. 2348--2356."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0434(1995)010<0620:APFCAT>2.0.CO;2"},{"key":"e_1_3_2_2_24_1","unstructured":"Jonathan Heek and Nal Kalchbrenner. 2019. Bayesian Inference for Large Scale Image Classification. (2019). arXiv:1908.03491.  Jonathan Heek and Nal Kalchbrenner. 2019. Bayesian Inference for Large Scale Image Classification. (2019). arXiv:1908.03491."},{"key":"e_1_3_2_2_25_1","volume-title":"International Air Transport Association","author":"IATA","year":"2021","unstructured":"IATA , International Air Transport Association . 2021 . https:\/\/www.iata.org\/ https:\/\/www.iata.org\/. IATA, International Air Transport Association. 2021. https:\/\/www.iata.org\/ https:\/\/www.iata.org\/."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2611567"},{"key":"e_1_3_2_2_27_1","volume-title":"Glow: Generative flow with invertible 1x1 convolutions. In NIPS . 10215--10224.","author":"Kingma Durk P","year":"2018","unstructured":"Durk P Kingma and Prafulla Dhariwal . 2018 . Glow: Generative flow with invertible 1x1 convolutions. In NIPS . 10215--10224. Durk P Kingma and Prafulla Dhariwal. 2018. Glow: Generative flow with invertible 1x1 convolutions. In NIPS . 10215--10224."},{"key":"e_1_3_2_2_28_1","volume-title":"Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_2_29_1","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. (2016).  Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. (2016)."},{"key":"e_1_3_2_2_30_1","unstructured":"Danijel Kivaranovic Kory D Johnson and Hannes Leeb. 2020. Adaptive Distribution-Free Prediction Intervals for Deep Networks. In AISTATS . 4346--4356.  Danijel Kivaranovic Kory D Johnson and Hannes Leeb. 2020. Adaptive Distribution-Free Prediction Intervals for Deep Networks. In AISTATS . 4346--4356."},{"key":"e_1_3_2_2_31_1","volume-title":"Quantile Regression","author":"Koenker R.","unstructured":"R. Koenker . 2005. Quantile Regression . Cambridge University Press . R. Koenker. 2005. Quantile Regression .Cambridge University Press."},{"key":"e_1_3_2_2_32_1","unstructured":"Balaji Lakshminarayanan Alexander Pritzel and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. In NIPS . 6402--6413.  Balaji Lakshminarayanan Alexander Pritzel and Charles Blundell. 2017. Simple and scalable predictive uncertainty estimation using deep ensembles. In NIPS . 6402--6413."},{"key":"e_1_3_2_2_33_1","volume-title":"Why M heads are better than one: Training a diverse ensemble of deep networks. arXiv:1511.06314","author":"Lee Stefan","year":"2015","unstructured":"Stefan Lee , Senthil Purushwalkam , Michael Cogswell , David Crandall , and Dhruv Batra . 2015. Why M heads are better than one: Training a diverse ensemble of deep networks. arXiv:1511.06314 ( 2015 ). Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David Crandall, and Dhruv Batra. 2015. Why M heads are better than one: Training a diverse ensemble of deep networks. arXiv:1511.06314 (2015)."},{"key":"e_1_3_2_2_34_1","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In ICLR .  Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In ICLR ."},{"key":"e_1_3_2_2_35_1","unstructured":"Haoxing Lin Rufan Bai Weijia Jia Xinyu Yang and Yongjian You. 2020. Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction. In ACM SIGKDD . 36--46.  Haoxing Lin Rufan Bai Weijia Jia Xinyu Yang and Yongjian You. 2020. Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction. In ACM SIGKDD . 36--46."},{"key":"e_1_3_2_2_36_1","unstructured":"G Liu and S Shuo. 2018. Air quality forecasting using convolutional LSTM.  G Liu and S Shuo. 2018. Air quality forecasting using convolutional LSTM."},{"key":"e_1_3_2_2_37_1","unstructured":"Christos Louizos and Max Welling. 2016. Structured and efficient variational deep learning with matrix gaussian posteriors. In ICML . 1708--1716.  Christos Louizos and Max Welling. 2016. Structured and efficient variational deep learning with matrix gaussian posteriors. In ICML . 1708--1716."},{"key":"e_1_3_2_2_38_1","unstructured":"Yi-An Ma Tianqi Chen and Emily Fox. 2015. A complete recipe for stochastic gradient MCMC. In NIPS. 2917--2925.  Yi-An Ma Tianqi Chen and Emily Fox. 2015. A complete recipe for stochastic gradient MCMC. In NIPS. 2917--2925."},{"key":"e_1_3_2_2_39_1","volume-title":"Fox","author":"Ma Yi-An","year":"2017","unstructured":"Yi-An Ma , Nicholas J. Foti , and Emily B . Fox . 2017 . Stochastic gradient MCMC Methods for hidden Markov models. In ICML . 2265--2274. Yi-An Ma, Nicholas J. Foti, and Emily B. Fox. 2017. Stochastic gradient MCMC Methods for hidden Markov models. In ICML . 2265--2274."},{"key":"e_1_3_2_2_40_1","unstructured":"Wesley J Maddox Pavel Izmailov Timur Garipov Dmitry P Vetrov and Andrew Gordon Wilson. 2019. A simple baseline for Bayesian uncertainty in deep learning. In NeurIPS . 13153--13164.  Wesley J Maddox Pavel Izmailov Timur Garipov Dmitry P Vetrov and Andrew Gordon Wilson. 2019. A simple baseline for Bayesian uncertainty in deep learning. In NeurIPS . 13153--13164."},{"key":"e_1_3_2_2_41_1","volume-title":"Scoring rules for continuous probability distributions. Management science","author":"Matheson James E","year":"1976","unstructured":"James E Matheson and Robert L Winkler . 1976. Scoring rules for continuous probability distributions. Management science , Vol. 22 , 10 ( 1976 ), 1087--1096. James E Matheson and Robert L Winkler. 1976. Scoring rules for continuous probability distributions. Management science , Vol. 22, 10 (1976), 1087--1096."},{"key":"e_1_3_2_2_42_1","volume-title":"Syed A Haque, Quan-Hui Liu, Kunpeng Mu, Xinyue Xiong, M Elizabeth Halloran, et almbox.","author":"Mistry Dina","year":"2020","unstructured":"Dina Mistry , Maria Litvinova , Matteo Chinazzi , Laura Fumanelli , Marcelo FC Gomes , Syed A Haque, Quan-Hui Liu, Kunpeng Mu, Xinyue Xiong, M Elizabeth Halloran, et almbox. 2020 . Inferring high-resolution human mixing patterns for disease modeling. arXiv preprint arXiv:2003.01214 (2020). Dina Mistry, Maria Litvinova, Matteo Chinazzi, Laura Fumanelli, Marcelo FC Gomes, Syed A Haque, Quan-Hui Liu, Kunpeng Mu, Xinyue Xiong, M Elizabeth Halloran, et almbox. 2020. Inferring high-resolution human mixing patterns for disease modeling. arXiv preprint arXiv:2003.01214 (2020)."},{"key":"e_1_3_2_2_43_1","volume-title":"Bayesian learning for neural networks","author":"Neal Radford M","unstructured":"Radford M Neal . 2012. Bayesian learning for neural networks . Vol. 118 . Springer Science & Business Media . Radford M Neal. 2012. Bayesian learning for neural networks . Vol. 118. Springer Science & Business Media."},{"key":"e_1_3_2_2_44_1","volume-title":"Aviation Worlwide Limited","author":"OAG","year":"2021","unstructured":"OAG , Aviation Worlwide Limited . 2021 . http:\/\/www.oag.com\/ http:\/\/www.oag.com\/. OAG, Aviation Worlwide Limited. 2021. http:\/\/www.oag.com\/ http:\/\/www.oag.com\/."},{"key":"e_1_3_2_2_45_1","unstructured":"Ian Osband Charles Blundell Alexander Pritzel and Benjamin Van Roy. 2016. Deep Exploration via Bootstrapped DQN. In NIPS. 4026--4034.  Ian Osband Charles Blundell Alexander Pritzel and Benjamin Van Roy. 2016. Deep Exploration via Bootstrapped DQN. In NIPS. 4026--4034."},{"key":"e_1_3_2_2_46_1","unstructured":"Tim Pearce Alexandra Brintrup Mohamed Zaki and Andy Neely. 2018. High-quality prediction intervals for deep learning: A distribution-free ensembled approach. In ICML. PMLR 4075--4084.  Tim Pearce Alexandra Brintrup Mohamed Zaki and Andy Neely. 2018. High-quality prediction intervals for deep learning: A distribution-free ensembled approach. In ICML. PMLR 4075--4084."},{"key":"e_1_3_2_2_47_1","unstructured":"Xin Qiu Elliot Meyerson and Risto Miikkulainen. 2019. Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I\/O Kernel. In ICLR .  Xin Qiu Elliot Meyerson and Risto Miikkulainen. 2019. Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I\/O Kernel. In ICLR ."},{"key":"e_1_3_2_2_48_1","volume-title":"Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082","author":"Rezende Danilo Jimenez","year":"2014","unstructured":"Danilo Jimenez Rezende , Shakir Mohamed , and Daan Wierstra . 2014. Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082 ( 2014 ). Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082 (2014)."},{"key":"e_1_3_2_2_49_1","unstructured":"Xiaocheng Shang Zhanxing Zhu Benedict Leimkuhler and Amos J Storkey. 2015. Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. In NIPS. 37--45.  Xiaocheng Shang Zhanxing Zhu Benedict Leimkuhler and Amos J Storkey. 2015. Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. In NIPS. 37--45."},{"key":"e_1_3_2_2_50_1","unstructured":"Alexander Shekhovtsov and Boris Flach. 2018. Feed-forward propagation in probabilistic neural networks with categorical and max layers. In ICLR .  Alexander Shekhovtsov and Boris Flach. 2018. Feed-forward propagation in probabilistic neural networks with categorical and max layers. In ICLR ."},{"key":"e_1_3_2_2_51_1","volume-title":"Amos Maritan, and Albert-L\u00e1 szl\u00f3 Barab\u00e1 si.","author":"Simini Filippo","year":"2012","unstructured":"Filippo Simini , Marta C Gonz\u00e1 lez , Amos Maritan, and Albert-L\u00e1 szl\u00f3 Barab\u00e1 si. 2012 . A universal model for mobility and migration patterns . Nature , Vol. 484 , 7392 (2012), 96--100. Filippo Simini, Marta C Gonz\u00e1 lez, Amos Maritan, and Albert-L\u00e1 szl\u00f3 Barab\u00e1 si. 2012. A universal model for mobility and migration patterns . Nature , Vol. 484, 7392 (2012), 96--100."},{"key":"e_1_3_2_2_52_1","unstructured":"Natasa Tagasovska and David Lopez-Paz. 2019. Single-model uncertainties for deep learning. In NeurIPS. 6417--6428.  Natasa Tagasovska and David Lopez-Paz. 2019. Single-model uncertainties for deep learning. In NeurIPS. 6417--6428."},{"key":"e_1_3_2_2_53_1","volume-title":"Nicola Perra, Vittoria Colizza, and Alessandro Vespignani.","author":"Tizzoni Michele","year":"2012","unstructured":"Michele Tizzoni , Paolo Bajardi , Chiara Poletto , Jos\u00e9 J Ramasco , Duygu Balcan , Bruno Goncc alves , Nicola Perra, Vittoria Colizza, and Alessandro Vespignani. 2012 . Real-time numerical forecast of global epidemic spreading: case study of 2009 A\/H1N1pdm. BMC medicine , Vol. 10, 1 (2012), 165. Michele Tizzoni, Paolo Bajardi, Chiara Poletto, Jos\u00e9 J Ramasco, Duygu Balcan, Bruno Goncc alves, Nicola Perra, Vittoria Colizza, and Alessandro Vespignani. 2012. Real-time numerical forecast of global epidemic spreading: case study of 2009 A\/H1N1pdm. BMC medicine , Vol. 10, 1 (2012), 165."},{"key":"e_1_3_2_2_54_1","unstructured":"US-EPA. 2012. Revised air quality standards for particle pollution and updates to the Air Quality Index (AQI).  US-EPA. 2012. Revised air quality standards for particle pollution and updates to the Air Quality Index (AQI)."},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"crossref","unstructured":"Thomas Vandal Evan Kodra Jennifer Dy Sangram Ganguly Ramakrishna Nemani and Auroop R Ganguly. 2018. Quantifying uncertainty in discrete-continuous and skewed data with Bayesian deep learning. In ACM SIGKDD. 2377--2386.  Thomas Vandal Evan Kodra Jennifer Dy Sangram Ganguly Ramakrishna Nemani and Auroop R Ganguly. 2018. 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NIPS , Vol. 29 (2016), 118 -- 126 . Hao Wang, Xingjian Shi, and Dit-Yan Yeung. 2016. Natural-parameter networks: A class of probabilistic neural networks. NIPS , Vol. 29 (2016), 118--126.","journal-title":"NIPS"},{"key":"e_1_3_2_2_58_1","volume-title":"Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. In NIPS . 879--888.","author":"Wang Yunbo","year":"2017","unstructured":"Yunbo Wang , Mingsheng Long , Jianmin Wang , Zhifeng Gao , and S Yu Philip . 2017 . Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. In NIPS . 879--888. Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and S Yu Philip. 2017. Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms. In NIPS . 879--888."},{"key":"e_1_3_2_2_59_1","unstructured":"Max Welling and Yee W Teh. 2011. Bayesian learning via stochastic gradient Langevin dynamics. In ICML. 681--688.  Max Welling and Yee W Teh. 2011. Bayesian learning via stochastic gradient Langevin dynamics. In ICML. 681--688."},{"key":"e_1_3_2_2_60_1","volume-title":"A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053","author":"Wen Ruofeng","year":"2017","unstructured":"Ruofeng Wen , Kari Torkkola , Balakrishnan Narayanaswamy , and Dhruv Madeka . 2017. A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053 ( 2017 ). Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka. 2017. A multi-horizon quantile recurrent forecaster. arXiv preprint arXiv:1711.11053 (2017)."},{"key":"e_1_3_2_2_61_1","volume-title":"Bayesian Deep Learning and a Probabilistic Perspective of Generalization. arXiv:2002.08791","author":"Wilson Andrew Gordon","year":"2020","unstructured":"Andrew Gordon Wilson and Pavel Izmailov . 2020. Bayesian Deep Learning and a Probabilistic Perspective of Generalization. arXiv:2002.08791 ( 2020 ). Andrew Gordon Wilson and Pavel Izmailov. 2020. 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Deep distributed fusion network for air quality prediction. In ACM SIGKDD . 965--973."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_2_67_1","volume-title":"Why gradient clipping accelerates training: A theoretical justification for adaptivity. arXiv:1905.11881","author":"Zhang Jingzhao","year":"2019","unstructured":"Jingzhao Zhang , Tianxing He , Suvrit Sra , and Ali Jadbabaie . 2019. Why gradient clipping accelerates training: A theoretical justification for adaptivity. arXiv:1905.11881 ( 2019 ). Jingzhao Zhang, Tianxing He, Suvrit Sra, and Ali Jadbabaie. 2019. 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