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Event Series Special Seminar Series

“Contextualized learning for adaptive yet persistent AI in biomedicine” | Ben Lengerich

Computer Science & Engineering Building (CSE), Room 1202

Abstract: "In biomedical data analysis, an emerging trend focuses on contextualizing observations within biological and real-world processes. This approach facilitates high-resolution, context-specific insights by integrating information across datasets, but it is difficult to design systems which both share information and dynamically adapt to context. Toward this aim, this presentation will examine “contextualized learning”, a meta-learning […]

Deep Latent Variable Models for Compression and Natural Science | Stephan Mandt

Computer Science & Engineering Building (CSE), Room 1202

Latent variable models have been an integral part of probabilistic machine learning, ranging from simple mixture models to variational autoencoders to powerful diffusion probabilistic models at the center of recent media attention. Perhaps less well-appreciated is the intimate connection between latent variable models and data compression, and the potential of these models for advancing natural science. This talk will explore these topics.