Classically, imitation learning algorithms have been developed for idealized situations, e.g., the demonstrations are often required to be collected in the exact same environment and usually include ...

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 ...

This schedule, and the links contained in it, are subject to change during the semester. Exam dates, however, are final. Reading assignments are generally from one of the required textbooks: the ...

Our students and faculty are changing the world through their contributions to computing education, research, and industry. These awards received by members of the UT Computer Science community make ...

Computing Across Disciplines is a three-part series that explores the interdisciplinary programs at UT that are shaping the ...

Deep learning neural network algorithms, including convolutional and recurrent networks, have risen to popularity in recent years. Along with this popularity has come a wide range of implementations ...

UT Austin’s Scott Aaronson and his former postdoctoral researcher, Shih-Han Hung, played a key role in demonstrating the ...

Current version of system Cmodels is 3.86.1. Starting version 3.81 of Cmodels, the system supports incremental answer set solving. (One may add constraints to a program on the fly.) The interface for ...

Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. In archaeology, we collect fragments of one or multiple objects at an archaeological site, and the ...