‍ 🎯 Deep Learning and Neural Networks Symposium and Workshop

👨🏻‍🎓 Speaker introduction:

Dr. Soheil Kolouri,
Research Scientist and Principal Investigator at HRL Laboratories, Malibu, California

Title: Deep Generative Modeling via Wasserstein Distances

Abstract: Deep generative models have become a cornerstone of modern machine learning. The celebrated generative adversarial networks (GANs) have notably contributed to the recent success of these models. However, GANs are also known to be notoriously difficult to optimize, and they are often not stable. Probability metrics, on the other hand, have proven themselves as a reliable alternative to adversarial networks, and provide a better geometric understanding of the problem. In this talk, I will focus on Wasserstein (GSW) distances, which emerge from the optimal transportation problem, discuss their limitations, and introduce Generalized Sliced Wasserstein distances as a remedy to alleviate some of these limitations. I will then review various applications of the GSW in deep generative modeling and transfer learning.

⭕️ Check our website for more information

⚙️ Organizers: Institute for Cognitive and Brain Sciences, Shahid Beheshti University and Loop Academy


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📢 @CMPLab

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