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DTSTART;TZID=America/New_York:20230503T123000
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SUMMARY:Generative Adversarial Networks (GANs): An Analytical Perspective
DESCRIPTION:Speaker: Xin Guo\, UC Berkeley \nTitle: Generative Adversarial Networks (GANs): An Analytical Perspective \nAbstract: Generative models have attracted intense interests recently. In this talk\, I will discuss one class of generative models\, Generative Adversarial Networks (GANs).  I will first provide a gentle review of the mathematical framework behind GANs. I will then proceed to discuss a few challenges in GANs training from an analytical perspective. I will finally report some recent progress for GANs training in terms of its stability and convergence analysis. \n 
URL:https://cmsa.fas.harvard.edu/event/collquium-5323/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Colloquium-05.03.2023.png
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SUMMARY:Random Neural Networks
DESCRIPTION:Probability Seminar \nSpeaker: Boris Hanin (Princeton)\n\nTitle: Random Neural Networks \nAbstract: Fully connected neural networks are described two by structural parameters: a depth L and a width N. In this talk\, I will present results and open questions about the asymptotic analysis of such networks with random weights and biases in the regime where N (and potentially L) are large. The first set of results are for deep linear networks\, which are simply products of L random matrices of size N x N. I’ll explain how the setting where the ratio L / N is fixed with both N and L large reveals a number of phenomena not present when only one of them is large. I will then state several results about non-linear networks in which this depth-to-width ratio L / N again plays a crucial role and gives an effective notion of depth for a random neural network.
URL:https://cmsa.fas.harvard.edu/event/probability-5323/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Probability Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Probability-Seminar-05.03.23.png
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