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  • CS Colloquium: Ruslan Salakhutdinov (Carnegie Mellon) - Learning Deep Unsupervised and Multimodal Models

    Tue, Jan 24, 2017 @ 04:00 PM - 05:20 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Ruslan Salakhutdinov, Carnegie Mellon

    Talk Title: Learning Deep Unsupervised and Multimodal Models

    Series: NVIDIA Distinguished Lecture Series in Machine Learning

    Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.

    In this talk I will first introduce a broad class of unsupervised deep learning models and show that they can learn useful hierarchical representations from large volumes of high-dimensional data with applications in information retrieval, object recognition, and speech perception. I will next introduce deep models that are capable of extracting a unified representation that fuses together multiple data modalities and present the Reverse Annealed Importance Sampling Estimator (RAISE) for evaluating these deep generative models. Finally, I will discuss models that can generate natural language descriptions (captions) of images and generate images from captions using attention, as well as introduce multiplicative and fine-grained gating mechanisms with application to reading comprehension.

    Part of NVIDIA Distinguished Lecture Series in Machine Learning.

    Biography: Ruslan Salakhutdinov received his PhD in computer science from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Departments of Statistics and Computer Science. In 2016 he joined the Machine Learning Department at Carnegie Mellon University as an Associate Professor. Ruslan\'s primary interests lie in deep learning, machine learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research and served on the senior programme committee of several learning conferences including NIPS and ICML. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, Canada Research Chair in Statistical Machine Learning, a recipient of the Early Researcher Award, Google Faculty Award, Nvidia\'s Pioneers of AI award, and is a Senior Fellow of the Canadian Institute for Advanced Research.

    Host: Yan Liu

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair


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