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SUMMARY:CS Colloquium: Jiaming Song (Stanford University) - Beyond Function Approximation: Compression, Inference, and Generation via Supervised Learning
DESCRIPTION:Speaker: Jiaming Song, Stanford University
Talk Title: Beyond Function Approximation: Compression, Inference, and Generation via Supervised Learning
Series: CS Colloquium
Abstract: Supervised learning methods have advanced considerably thanks to deep function approximators. However, important problems such as compression, probabilistic inference, and generative modeling cannot be directly addressed by supervised learning. At the core, these problems involve estimating (and optimizing) a suitable notion of distance between two probability distributions, which is challenging in high-dimensional spaces. In this talk, I will propose techniques to estimate and optimize divergences more effectively by leveraging advances in supervised learning. I will describe an algorithm for estimating mutual information that approaches a fundamental limit of all valid lower bound estimators and can empirically compress neural networks by up to 70% without losing accuracy. I will also show how these techniques can be used to accelerate probabilistic inference algorithms that have been developed for decades by nearly 10x, improve generative modeling and infer suitable rewards for sequential decision making. \n
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This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Jiaming Song is a fifth-year Ph.D. candidate in the Computer Science Department at Stanford University, advised by Stefano Ermon. His research focuses on learning and inference algorithms for deep probabilistic models with applications in unsupervised representation learning, generative modeling, and inverse reinforcement learning. He received his B.Eng degree in Computer Science from Tsinghua University in 2016. He was a recipient of the Qualcomm Innovation Fellowship.
Host: Bistra Dilkina
DTSTART:20210311T110000
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DTEND:20210311T120000
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