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UT Computer Science Summer Academies

Discover a unique summer learning experience at our academies on the UT campus. Tailored for high school students entering grades 10–12, each program is taught by UT faculty and focuses on a ...
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UTCS Freshman Launches Bid for Austin City Council

While most college freshmen are settling into campus life—choosing majors, meeting new friends and finding their footing—one UTCS student has his sights on a broader goal: a seat on the Austin City ...
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Cache Replacement Competition

07/03/2017 - Professor Calvin Lin and grad student Akanksha Jain won the 2nd Cache Replacement Competition, which was held recently in conjunction with the International Symposium on Computer ...
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Transfer Learning for Reinforcement Learning Domains: A Survey

Transfer Learning for Reinforcement Learning Domains: A Survey. Matthew E. Taylor and Peter Stone. Journal of Machine Learning Research, 10(1):1633–1685, 2009.
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Transfer Learning via Inter-Task Mappings for Temporal Difference Learning

Transfer Learning via Inter-Task Mappings for Temporal Difference Learning. Matthew E. Taylor, Peter Stone, and Yaxin Liu. Journal of Machine Learning Research, 8(1):2125–2167, 2007.
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Deep Reinforcement Learning in Parameterized Action Space

Recent work has shown that deep neural networks are capable ofapproximating both value functions and policies in reinforcementlearning domains featuring continuous state and actionspaces. However, to ...
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Marginal Cost Pricing for System Optimal Traffic Assignment with Recourse under Supply-Side Uncertainty

Transportation networks are often subject to fluctuations in supply-side parameters such as capacity and free-flow travel time due to factors such as incidents, poor weather, and bottlenecks. In such ...
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Multiagent Systems: A survey from a machine learning perspective

Multiagent Systems: A survey from a machine learning perspective. Peter Stone and Manuela Veloso. Autonomous Robots, 8(3):345–383, July 2000. @Article(MASsurvey, Author="Peter Stone and Manuela Veloso ...
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A collective AI via lifelong learning and sharing at the edge

One vision of a future artificial intelligence (AI) is where many separate unitscan learn independently over a lifetime and share their knowledge with eachother. The synergy between lifelong learning ...
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Learning to Interpret Natural Language Commands through Human-Robot Dialog

Learning to Interpret Natural Language Commands through Human-Robot Dialog. Jesse Thomason, Shiqi Zhang, Raymond Mooney, and Peter Stone. In Proceedings of the 2015 International Joint Conference on ...
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"What's That Robot Doing Here?": Factors Influencing Perceptions Of Incidental Encounters With Autonomous Quadruped Robots

Elliott Hauser, Yao-Cheng Chan, Geethika Hemkumar, Daksh Dua, Parth Chonkar, Efren Mendoza Enriquez, Tiffany Kao, Shikhar Gupta, Huihai Wang, Justin Hart, Reuth Mirsky, Joydeep Biswas, Junfeng Jiao, ...