Zobrazeno 1 - 10
of 238
pro vyhledávání: '"Watson, Joe"'
Autor:
Watson, Joe, Song, Chen, Weeger, Oliver, Gruner, Theo, Le, An T., Hansel, Kay, Hendawy, Ahmed, Arenz, Oleg, Trojak, Will, Cranmer, Miles, D'Eramo, Carlo, Bülow, Fabian, Goyal, Tanmay, Peters, Jan, Hoffman, Martin W.
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to the
Externí odkaz:
http://arxiv.org/abs/2408.09840
Bayesian deep learning approaches assume model parameters to be latent random variables and infer posterior distributions to quantify uncertainty, increase safety and trust, and prevent overconfident and unpredictable behavior. However, weight-space
Externí odkaz:
http://arxiv.org/abs/2307.06055
Imitation learning methods seek to learn from an expert either through behavioral cloning (BC) of the policy or inverse reinforcement learning (IRL) of the reward. Such methods enable agents to learn complex tasks from humans that are difficult to ca
Externí odkaz:
http://arxiv.org/abs/2305.16498
The preferential siting of the locations of monitors of hazardous environmental fields can lead to the serious underestimation of the impacts of those fields. In particular, human health effects can be severely underestimated when standard statistica
Externí odkaz:
http://arxiv.org/abs/2304.10006
Autor:
Watson, Joe1 (AUTHOR) j.watson@jbs.cam.ac.uk, van der Linden, Sander2 (AUTHOR), Watson, Michael3 (AUTHOR), Stillwell, David1,4 (AUTHOR)
Publikováno v:
Scientific Reports. 9/16/2024, Vol. 14 Issue 1, p1-10. 10p.
Autor:
Watson, Joe, Peters, Jan
Monte Carlo methods have become increasingly relevant for control of non-differentiable systems, approximate dynamics models and learning from data. These methods scale to high-dimensional spaces and are effective at the non-convex optimizations ofte
Externí odkaz:
http://arxiv.org/abs/2210.03512
Obtaining dynamics models is essential for robotics to achieve accurate model-based controllers and simulators for planning. The dynamics models are typically obtained using model specification of the manufacturer or simple numerical methods such as
Externí odkaz:
http://arxiv.org/abs/2110.12422
Autor:
Bauer, Stefan, Widmaier, Felix, Wüthrich, Manuel, Buchholz, Annika, Stark, Sebastian, Goyal, Anirudh, Steinbrenner, Thomas, Akpo, Joel, Joshi, Shruti, Berenz, Vincent, Agrawal, Vaibhav, Funk, Niklas, De Jesus, Julen Urain, Peters, Jan, Watson, Joe, Chen, Claire, Srinivasan, Krishnan, Zhang, Junwu, Zhang, Jeffrey, Walter, Matthew R., Madan, Rishabh, Schaff, Charles, Maeda, Takahiro, Yoneda, Takuma, Yarats, Denis, Allshire, Arthur, Gordon, Ethan K., Bhattacharjee, Tapomayukh, Srinivasa, Siddhartha S., Garg, Animesh, Sikchi, Harshit, Wang, Jilong, Yao, Qingfeng, Yang, Shuyu, McCarthy, Robert, Sanchez, Francisco Roldan, Wang, Qiang, Bulens, David Cordova, McGuinness, Kevin, O'Connor, Noel, Redmond, Stephen J., Schölkopf, Bernhard
Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designed and built robotic platforms that are hosted at MPI for Intelligent S
Externí odkaz:
http://arxiv.org/abs/2109.10957
Optimal control under uncertainty is a prevailing challenge for many reasons. One of the critical difficulties lies in producing tractable solutions for the underlying stochastic optimization problem. We show how advanced approximate inference techni
Externí odkaz:
http://arxiv.org/abs/2105.07693