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pro vyhledávání: '"Barnes, Matt"'
Autor:
Barnes, Matt, Abueg, Matthew, Lange, Oliver F., Deeds, Matt, Trader, Jason, Molitor, Denali, Wulfmeier, Markus, O'Banion, Shawn
Inverse reinforcement learning (IRL) offers a powerful and general framework for learning humans' latent preferences in route recommendation, yet no approach has successfully addressed planetary-scale problems with hundreds of millions of states and
Externí odkaz:
http://arxiv.org/abs/2305.11290
Autor:
Liu, Luyang, Racz, David, Vaillancourt, Kara, Michelman, Julie, Barnes, Matt, Mellem, Stefan, Eastham, Paul, Green, Bradley, Armstrong, Charles, Bal, Rishi, O'Banion, Shawn, Guo, Feng
Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which makes it difficult to achieve a fine-grain understanding of crash caus
Externí odkaz:
http://arxiv.org/abs/2202.01934
Autor:
Kapoor, Amol, Ben, Xue, Liu, Luyang, Perozzi, Bryan, Barnes, Matt, Blais, Martin, O'Banion, Shawn
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting models, the proposed approach learns from a single large-scale spati
Externí odkaz:
http://arxiv.org/abs/2007.03113
Publikováno v:
NeurIPS 2019
In many environments, only a relatively small subset of the complete state space is necessary in order to accomplish a given task. We develop a simple technique using emergency stops (e-stops) to exploit this phenomenon. Using e-stops significantly i
Externí odkaz:
http://arxiv.org/abs/1912.01649
Autor:
Gordon, Ethan K., Meng, Xiang, Barnes, Matt, Bhattacharjee, Tapomayukh, Srinivasa, Siddhartha S.
A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items. It must adapt to changing user food preferences under uncertain visual and physical environments. Different food items in different environmental co
Externí odkaz:
http://arxiv.org/abs/1908.07088
Autor:
Ke, Liyiming, Choudhury, Sanjiban, Barnes, Matt, Sun, Wen, Lee, Gilwoo, Srinivasa, Siddhartha
We address the problem of imitation learning with multi-modal demonstrations. Instead of attempting to learn all modes, we argue that in many tasks it is sufficient to imitate any one of them. We show that the state-of-the-art methods such as GAIL an
Externí odkaz:
http://arxiv.org/abs/1905.12888
Autor:
Liu, Luyang, Racz, David, Vaillancourt, Kara, Michelman, Julie, Barnes, Matt, Mellem, Stefan, Eastham, Paul, Green, Bradley, Armstrong, Charles, Bal, Rishi, O’Banion, Shawn, Guo, Feng
Publikováno v:
In Transportation Research Part C January 2023 146
Akademický článek
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