[41] Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055, 2018.
[42] Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Kwang-Ting Cheng, and Jian Sun. Metapruning: Meta learning for automatic neural network channel pruning. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3296-3305, 2019.
[43] Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang. Learning efficient convolutional networks through network slimming. In Proceedings of the IEEE international conference on computer vision, pages 2736-2744, 2017.
[44] Lennart Ljung. System identification. Springer, 1998.
[45] Miles Macklin, Matthias Müller, Nuttapong Chentanez, and Tae-Yong Kim. Unified particle physics for real-time applications. ACM Transactions on Graphics (TOG), 33(4):1-12, 2014.
[46] Chris J Maddison, Andriy Mnih, and Yee Whye Teh. The concrete distribution: A continuous relaxation of discrete random variables. arXiv preprint arXiv:1611.00712, 2016.
[47] Lucas Manuelli, Yunzhu Li, Pete Florence, and Russ Tedrake. Keypoints into the future: Self-supervised correspondence in model-based reinforcement learning. arXiv preprint arXiv:2009.05085, 2020.
[48] Tobia Marcucci and Russ Tedrake. Mixed-integer formulations for optimal control of piecewise-affine systems. In Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, pages 230–239, 2019.
[49] Tobia Marcucci and Russ Tedrake. Warm start of mixed-integer programs for model predictive control of hybrid systems. IEEE Transactions on Automatic Control, 2020.
[50] Tobia Marcucci, Jack Umenberger, Pablo A Parrilo, and Russ Tedrake. Shortest paths in graphs of convex sets. arXiv preprint arXiv:2101.11565, 2021.
[51] Microsoft. Neural Network Intelligence, 1 2021. URL https://github.com/microsoft/nni.
[52] Peter Mitrano, Dale McConachie, and Dmitry Berenson. Learning where to trust unreliable models in an unstructured world for deformable object manipulation. Science Robotics, 6(54): eabd8170, 2021.
[53] Thomas M Moerland, Joost Broekens, and Catholijn M Jonker. Model-based reinforcement learning: A survey. arXiv preprint arXiv:2006.16712, 2020.
[54] Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz. Pruning convolutional neural networks for resource efficient inference. arXiv preprint arXiv:1611.06440, 2016.
[55] Bruce Moore. Principal component analysis in linear systems: Controllability, observability, and model reduction. IEEE transactions on automatic control, 26(1):17-32, 1981.
[56] Anusha Nagabandi, Kurt Konolige, Sergey Levine, and Vikash Kumar. Deep dynamics models for learning dexterous manipulation. In Conference on Robot Learning, pages 1101-1112. PMLR, 2020.
[57] Reuven Y Rubinstein and Dirk P Kroese. The cross-entropy method: a unified approach to combinatorial optimization, Monte-Carlo simulation and machine learning. Springer Science & Business Media, 2013.
[58] Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al. Mastering atari, go, chess and shogi by planning with a learned model. Nature, 588(7839):604-609, 2020.
[59] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.