Found 5 results.

generative adversarial nets

Author(s): Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
Venue: Neural Information Processing Systems Conference (NeurIPS)
Year Published: 2014
Keywords: unsupervised learning, neural networks, gaussians
Expert Opinion: Because it introduces a new way of learning that shows a substantially improved behavior.

pattern recognition and machine learning

Author(s): Christopher M. Bishop
Venue: Book
Year Published: 2006
Keywords: probabilistic models, gaussians, unsupervised learning, reinforcement learning
Expert Opinion: A wonderful overview of pattern matching/machine learning minus DNNs.

reinforcement learning: an introduction

Author(s): Richard S. Sutton and Andrew G. Barto
Venue: Book
Year Published: 2018
Keywords: mobile robots, reinforcement learning, unsupervised learning, optimal control, genetic algorithms
Expert Opinion: Great introductory text book to the underpinnings of of a lot of the modern approaches in ML/RL for robotics.

model learning for robot control: a survey

Author(s): Duy Nguyen-Tuong, Jan Peters
Venue: Cognitive Science
Year Published: 2011
Keywords: gaussians, survey, dynamical systems, optimal control, unsupervised learning, reinforcement learning
Expert Opinion: The only non-RL paper on my list :). Modelling of robots is part of both very classical control approaches as well as modern learning approaches. There are many excellent papers, I chose this one for providing a wide overview. One of my favourite papers on this topic, by the same authors, included in this survey combines insights from analytic modelling (allowing fast identification of a small set of parameters) with Gaussian process modelling (allowing precise and flexible modelling, but at the cost of requiring more data). I chose this survey instead, as it provides a wider overview and is thus something I would be more likely to suggest to a student or mentee to get a wider overview.