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Texas Symposium on Machine Learning, Responsible AI & Robotics
Join Texas Robotics, the Machine Learning Lab, and Good Systems on March 3 & 4 for a two-day symposium exploring responsible innovation in AI and Robotics. Discover boundary-breaking research insights and enjoy thought-provoking talks...
Upcoming Events
- February1312:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: Particle physicists developed an algorithm called COWs (Customized Orthogonal Weights) for separating signals from backgrounds in certain…
- February2012:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: To assess the ability of current AI systems to correctly answer research-level mathematics questions, we share a set of...
- February2712:15 - 1:15pm
IFML Seminar
Event DetailsAbstract: Sampling is a fundamental algorithmic task with many connections to optimization. In this talk, we survey a recent algorithm...
- March3throughMarch4Event Details
Join Texas Robotics, the Machine Learning Lab, and Good Systems on March 3 & 4 for a two-day symposium exploring...
Past Events
- December45:30 - 8 pm
Public Lecture
Event DetailsPublic event hosted by IBM, the Austin AI Alliance, and the Global AI Alliance (co-founded by IBM). Our Deep Proteins...
- November1512:15 - 1:15pm
IFML Seminar
Event DetailsSpeaker Bio: Zak Mhammedi is a Research Scientist at Google Research , focusing on reinforcement learning and optimization. He completed...
- November13throughNovember15Event Details
Join us for UT Austin’s Year of AI celebration as we showcase the best ideas, innovations and inspiration in the...
- November112:15 - 1 pm
IFML Seminar
Event DetailsSpeaker Bio: Cristopher Moore received his B.A. in Physics, Mathematics, and Integrated Science from Northwestern University, and his Ph.D. in...
- October2512:15 - 1 pm
IFML Seminar
Event DetailsAbstract: Reinforcement learning often faces a trade-off between model flexibility and computational tractability. Flexible models can capture complex…
- October1712:15 - 1 pm
IFML Seminar
Event DetailsAbstract: I will argue that deep networks work well because of a characteristic structure in the space of learnable tasks...
