A curated list of resources dedicated to theory and applications of machine learning to Control Theory and Engineering (Emphasis on Large Scale Control and Deep Learning)
- Maintainers
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- Continuous Action Reinforcement Learning for Control-Affine Systems with Unknown Dynamics
- Preference-balancing Motion Planning under Stochastic Disturbances
- Adaptive Human-Inspired Compliant Contact Primitives to Perform Surface-Surface Contact under Uncertainty
- Learning Potential Functions from Human Demonstrations with Encapsulated Dynamic and Compliant Behaviors
- Learning Stable Non-Linear Dynamical Systems with Gaussian Mixture Models
- Learning Control Lyapunov Function to Ensure Stability of Dynamical System-based Robot Reaching Motions
- A Dynamical System Approach to Realtime Obstacle Avoidance
- FeUdal Networks for Hierarchical Reinforcement Learning
- Optnet: Differentiable Optimization as a Layer in Neural Networks
- qpth: A fast and differentiable Quadratic Programming solver for PyTorch
- How hard is it to cross the room? - Training (Recurrent) Neural Networks to steer a UAV
- Neural Episodic Control
- Inferring and Executing Programs for Visual Reasoning
- Dynamical Systems approach to Learn Robot Motions
- Learning representations by backpropagating errors
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Foundational Papers on Modern Machine Learning
- Course on Information Theory, Pattern Recognition, and Neural Networks
- Approximation by Superpositions of a Sigmoidal Function. Approximation Theory and Its Applications
- On the approximate realization of continuous mappings by neural networks..
- Parallel networks that learn to pronounce English text. Complex Systems
- Dynamical Systems approach to Learn Robot Motions
- Ecole Polytechnique Federale de Lausanne, LASA Laboratory
- Continuous Action Reinforcement Learning for Control-Affine Systems
with Unknown Dynamics
- Author: Aleksandra Faust
- Institution Affiliation: University of New Mexico
- Preference-balancing Motion Planning under Stochastic Disturbances
- Author: Aleksandra Faust
- Institution Affiliation: University of New Mexico
- Adaptive Human-Inspired Compliant Contact Primitives to Perform Surface-Surface Contact under Uncertainty
- Authors: S.M. Khansari-Zadeh, E. Klingbeil, and O. Khatib (2016)
- Institution: Stanford
- Journal: The International Journal of Robotics Research
- Learning Potential Functions from Human Demonstrations with Encapsulated Dynamic and Compliant Behaviors
- Authors: S.M. Khansari-Zadeh and O. Khatib (2015)
- Institution: Stanford
- Journal: Autonomous Robots.
- Learning Stable Non-Linear Dynamical Systems with Gaussian Mixture Models
- Institution: Stanford
- Authors: S.M. Khansari-Zadeh and A. Billard (2011), L,
- Journal: IEEE Transaction on Robotics, vol. 27, num 5, p. 943-957.
- Learning Control Lyapunov Function to Ensure Stability of Dynamical System-based Robot Reaching Motions
- Institution: Stanford
- Authors: S.M. Khansari-Zadeh and A. Billard (2014),
- Journal: Robotics and Autonomous Systems, vol. 62, num 6, p. 752-765.
- A Dynamical System Approach to Realtime Obstacle Avoidance
- Authors: S.M. Khansari-Zadeh and A. Billard (2012),
- Journal: Autonomous Robots, vol. 32, num 4, p. 433-454.
- ADAPT: Zero-Shot Adaptive Policy Transfer for Stochastic Dynamical Systems
- Authors: James Harrison, Animesh Garg, Boris Ivanovic, Yuke Zhu, Silvio Savarese, Li Fei-Fei, Marco Pavone
- Institute: Stanford
- Emergence of Locomotion Behaviours in Rich Environments
- Authors: Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, Ali Eslami, Martin Riedmiller, David Silver
- Institution: DeepMind
- Input Convex Neural Networks
- Authors: Brandon Amos, J. Zico Kolter
- Institution: CMU
- FeUdal Networks for Hierarchical Reinforcement Learning
- Authors: Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu
- Institution: Google
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Optnet: Differentiable Optimization as a Layer in Neural Networks
- Code
- Authors: Brandon Amos and J. Zico Kolter. (3/2017).
- Institution Affiliation: CMU
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- Vanderberghe, UCLA
- qpth: A fast and differentiable Quadratic Programming solver for PyTorch
- Code
- Authors: Brandon Amos and J. Zico Kolter. (3/2017).
- Institution Affiliation: CMU
- How hard is it to cross the room? - Training (Recurrent) Neural Networks to steer a UAV
- Authors: Klaas Kelchtermans and Tinne Tuytelaars. (3/2017).
- Institution Affiliation: KU Leuven
- Neural Episodic Control
- Authors: Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell
- Google/DeepMind
- Inferring and Executing Programs for Visual Reasoning
- Authors: Justin C. Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick
- Course on Information Theory, Pattern Recognition, and Neural Networks
- Author: Sir David MacKay
- Institution Affiliation: University of Cambridge
- Approximation by Superpositions of a Sigmoidal Function. Approximation Theory and Its Applications
- Author: Cybenko, G. (1993).
- Institution Affiliation: University of Illinois
- On the approximate realization of continuous mappings by neural networks.
- Author: Ken-Ichi Funahashi, 5/1988
- Institution Affiliation: CMU
- Parallel networks that learn to pronounce English text. Complex Systems
- Sejnowski, T. J., & Rosenberg, C. R. (1987).
- Institution Affiliation: Johns Hopkins University/Princeton University
- Learning representations by backpropagating errors.
- Rumelhart, D. E., & Hinton, G. E. (1986).
- Institution Affiliation: UToronto