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Imitation learning enables high-fidelity, vision-based learning of policies within rich, photorealistic environments.
Latent odes for irregularly-sampled time series
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Is imitation learning the route to humanoid robots?
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Charles Kemp, Noah D Goodman, and Joshua B Tenenbaum · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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From ordinary differential equations to structural causal models: the deterministic case
Joris M Mooij, Dominik Janzing, and Bernhard Schölkopf · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Probabilistic reasoning in intelligent systems: networks of plausible inference
Judea Pearl · 2014
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Visualbackprop: efficient visualization of cnns
Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Larry Jackel, Urs Muller, and Karol Zieba · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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From deterministic odes to dynamic structural causal models
Paul K Rubenstein, Stephan Bongers, Bernhard Schölkopf, and Joris M Mooij · 2016
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End-to-end differentiable adversarial imitation learning
Nir Baram, Oron Anschel, Itai Caspi, and Shie Mannor · 2017
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Dynamic models of large-scale brain activity
Michael Breakspear · 2017
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Yan Duan, Marcin Andrychowicz, Bradly C Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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Neuroscience-inspired artificial intelligence
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Discrete event, continuous time rnns
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Neural topological slam for visual navigation
Devendra Singh Chaplot, Ruslan Salakhutdinov, Abhinav Gupta, and Saurabh Gupta · 2020
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Learning by cheating
Dian Chen, Brady Zhou, Vladlen Koltun, and Philipp Krähenbühl · 2020
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An imitation from observation approach to transfer learning with dynamics mismatch
Siddharth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, Peter Stone, and AI Sony · 2020
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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
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On robustness of neural ordinary differential equations
YAN Hanshu, DU Jiawei, TAN Vincent, and FENG Jiashi · 2020
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The natural lottery ticket winner: Reinforcement learning with ordinary neural circuits
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Maithra Raghu, Ben Poole, Jon Kleinberg, Surya Ganguli, and Jascha Sohl-Dickstein · 2017
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State aware imitation learning
Yannick Schroecker and Charles Isbell · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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Mathematical theory of optimal processes
Lev Semenovich Pontryagin · 2018
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Openworm: overview and recent advances in integrative biological simulation of caenorhabditis elegans
Gopal P Sarma, Chee Wai Lee, Tom Portegys, Vahid Ghayoomie, Travis Jacobs, Bradly Alicea, Matteo Cantarelli, Michael Currie, Richard C Gerkin, Shane Gingell, et al · 2018
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Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu · 2020
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Learning to control pdes with differentiable physics
Philipp Holl, Vladlen Koltun, and Nils Thuerey · 2020
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Dynamic representations in networked neural systems
Harang Ju and Danielle S Bassett · 2020
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Algorithmic recourse: from counterfactual explanations to interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 2020
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Elia Kaufmann, Antonio Loquercio, René Ranftl, Matthias Müller, Vladlen Koltun, and Davide Scaramuzza · 2020
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Learning long-term dependencies in irregularly-sampled time series
Mathias Lechner and Ramin Hasani · 2020
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Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme
Mathias Lechner, Ramin Hasani, Daniela Rus, and Radu Grosu · 2020
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Learning quadrupedal locomotion over challenging terrain
Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 2020
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Airsim drone racing lab
Ratnesh Madaan, Nicholas Gyde, Sai Vemprala, Matthew Brown, Keiko Nagami, Tim Taubner, Eric Cristofalo, Davide Scaramuzza, Mac Schwager, and Ashish Kapoor · 2020
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Dissecting neural odes
Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, and Hajime Asma · 2020
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Interpretable machine learning
Christoph Molnar · 2020
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Diverse and admissible trajectory forecasting through multimodal context understanding
Seong Hyeon Park, Gyubok Lee, Jimin Seo, Manoj Bhat, Minseok Kang, Jonathan Francis, Ashwin Jadhav, Paul Pu Liang, and Louis-Philippe Morency · 2020
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Snode: Spectral discretization of neural odes for system identification
Alessio Quaglino, Marco Gallieri, Jonathan Masci, and Jan KoutnÃk · 2020
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Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
Sebastian Weichwald, Martin E Jakobsen, Phillip B Mogensen, Lasse Petersen, Nikolaj Thams, and Gherardo Varando · 2020
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Fighting copycat agents in behavioral cloning from observation histories
Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, and Yang Gao · 2020
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Model-based versus model-free deep reinforcement learning for autonomous racing cars
Axel Brunnbauer, Luigi Berducci, Andreas Brandstätter, Mathias Lechner, Ramin Hasani, Daniela Rus, and Radu Grosu · 2021
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Lipschitz recurrent neural networks
N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, and Michael W. Mahoney · 2021
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Gotube: Scalable stochastic verification of continuous-depth models
Sophie Gruenbacher, Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas A Henzinger, Scott Smolka, and Radu Grosu · 2021
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On the verification of neural odes with stochastic guarantees
Sophie Grunbacher, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A. Smolka, and Radu Grosu · 2021
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Adversarial training is not ready for robot learning
Mathias Lechner, Ramin Hasani, Radu Grosu, Daniela Rus, and Thomas A Henzinger · 2021
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Sparse flows: Pruning continuous-depth models
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Coupled oscillatory recurrent neural network (co{rnn}): An accurate and (gradient) stable architecture for learning long time dependencies
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