[23] Song Han, Jeff Pool, John Tran, and William Dally. Learning both weights and connections for efficient neural network. Advances in neural information processing systems, 28, 2015.
[24] Yihui He, Xiangyu Zhang, and Jian Sun. Channel pruning for accelerating very deep neural networks. In Proceedings of the IEEE international conference on computer vision, pages 1389-1397, 2017.
[25] Arthur J Helmicki, Clas A Jacobson, and Carl N Nett. Control oriented system identification: a worst-case/deterministic approach in h/sub infinity. IEEE Transactions on Automatic control, 36(10):1163-1176, 1991.
[26] François Robert Hogan and Alberto Rodriguez. Feedback control of the pusher-slider system: A story of hybrid and underactuated contact dynamics. arXiv preprint arXiv:1611.08268, 2016.
[27] Zixuan Huang, Xingyu Lin, and David Held. Mesh-based dynamics model with occlusion reasoning for cloth manipulation. In Robotics: Science and Systems (RSS), 2022.
[28] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016.
[29] Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine. When to trust your model: Model-based policy optimization. CoRR, abs/1906.08253, 2019. URL http://arxiv.org/abs/1906.08253.
[30] Natalie A Jones, Helen Ross, Timothy Lynam, Pascal Perez, and Anne Leitch. Mental models: an interdisciplinary synthesis of theory and methods. *Ecology and Society*, 16(1), 2011.
[31] Tejas D Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, and Volodymyr Mnih. Unsupervised learning of object keypoints for perception and control. Advances in neural information processing systems, 32:10724-10734, 2019.
[32] Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman. Building machines that learn and think like people. Behavioral and brain sciences, 40, 2017.
[33] Yann LeCun, John S Denker, and Sara A Solla. Optimal brain damage. In Advances in neural information processing systems, pages 598-605, 1990.
[34] Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter. Learning quadrupedal locomotion over challenging terrain. Science robotics, 5(47), 2020.
[35] Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf. Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710, 2016.
[36] Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B Tenenbaum, and Antonio Torralba. Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids. arXiv preprint arXiv:1810.01566, 2018.
[37] Yunzhu Li, Jiajun Wu, Jun-Yan Zhu, Joshua B Tenenbaum, Antonio Torralba, and Russ Tedrake. Propagation networks for model-based control under partial observation. In 2019 International Conference on Robotics and Automation (ICRA), pages 1205-1211. IEEE, 2019.
[38] Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi, and Antonio Torralba. Learning compositional koopman operators for model-based control. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=H1ldzA4tPr.
[39] Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, and Animesh Garg. Causal discovery in physical systems from videos. Advances in Neural Information Processing Systems, 33, 2020.
[40] Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy. Progressive neural architecture search. In Proceedings of the European conference on computer vision (ECCV), pages 19–34, 2018.