Transfer learning is a method where an agent reuses knowledge learned in a source task to improve learning on a target task. Recent work has shown that transfer learning can be extended to the idea of ...

Degree requirements for each plan can vary greatly by catalog year. Degree requirements on older catalogs may include courses that are no longer offered, due to the change in CS curriculum. For ...

As president in my sophomore year, CSB tried to do a couple of things. One was social bonding—bringing everyone together through socials and having fun. The other part was sourcing career networking ...

Though computers have surpassed humans at many tasks, especially computationally intensive ones, there are many tasks for which human expertise remains necessary and/or useful. For such tasks, it is ...

Multiagent Traffic Management: A Reservation-Based Intersection Control Mechanism. Kurt Dresner and Peter Stone. In The Third International Joint Conference on Autonomous Agents and Multiagent Systems ...

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

My research interests are in the area of machine learning for speech, language, and sound processing. I am particularly interested in multimodality and unsupervised ...

PhD student Yeonju Ro received the 2024 IBM PhD Fellowship award with an endowment of $40,000.

In the past five years, Isler headed research for Samsung AI Center in New York, where he helped develop AI robots for household use. On top of teaching, Isler will continue his research at UT and ...

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UT Austin is celebrating 100 years of quantum science, highlighting its impact on computing, clean energy, and medicine. Faculty member Scott Aaronson is advancing quantum computing by developing ...