{ "143254-21892556": { "datetime_modified":"20260109T114337", "datetime_start":"20260116T100000", "datetime_end":"20260116T110000", "has_end_time":1, "date_start":"2026-01-16", "date_end":"2026-01-16", "time_start":"10:00:00", "time_end":"11:00:00", "time_zone":"America\/Detroit", "event_title":"Statistics Department Seminar Series: Jian Kang, Professor & Associate Chair for Research, Biostatistics, University of Michigan", "occurrence_title":"", "combined_title":"Statistics Department Seminar Series: Jian Kang, Professor & Associate Chair for Research, Biostatistics, University of Michigan: \"Bayesian Deep Noise Neural Networks: Uncertainty Quantification, Density Regression, and Generative Modeling\"", "event_subtitle":"\"Bayesian Deep Noise Neural Networks: Uncertainty Quantification, Density Regression, and Generative Modeling\"", "event_type":"Workshop \/ Seminar", "event_type_id":"21", "description":"Abstract: Deep generative models, such as Variational Autoencoders (VAE) and diffusion probabilistic models, have transformed high-dimensional data modeling. However, these approaches often rely on variational approximations or computationally intensive ordinary differential equation (ODE) solvers, trading exact Bayesian inference for scalability. In this talk, I present the Bayesian Deep Noise Neural Network (B-DeepNoise), a framework originally developed for density regression that possesses inherent yet under-explored generative capabilities. Unlike standard Bayesian neural networks that place priors only on network weights, the B-DeepNoise framework injects stochastic noise into every hidden layer of a deep architecture. We show that this construction is mathematically equivalent to a deep hierarchical latent variable model, yielding rich conditional distributions through layer-wise noise propagation. By exploiting piecewise-linear activation functions, specifically ReLU function, we derive a closed-form Gibbs sampling algorithm that enables asymptotically exact posterior inference, avoiding the approximation errors commonly associated with variational methods. I will demonstrate how this framework unifies three closely related tasks: (1) uncertainty quantification in regression, (2) density regression for complex conditional distributions, and (3) extensions to generative modeling, where layer-wise noise injection enables flexible sample generation and data imputation. These results bridge flexible deep learning architectures with rigorous Bayesian inference and computational statistics, providing a principled approach to density learning and generative modeling.", "occurrence_notes":null, "guid":"143254-21892556@events.umich.edu", "permalink":"http:\/\/events.umich.edu\/event\/143254", "building_id":"1000167", "building_name":"West Hall", "campus_maps_id":"163", "room":"340", "location_name":"West Hall", "has_livestream":0, "cost":"", "tags":["seminar"], "website":"https:\/\/lsa.umich.edu\/stats", "sponsors":[ { "group_name":"Department of Statistics", "group_id":"3473", "website":"" }, { "group_name":"Department of Statistics Seminar Series", "group_id":"3585", "website":"" } ], "image_url":"https:\/\/events.umich.edu\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "styled_images":{ "event_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_thumb\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_large":"https:\/\/events.umich.edu\/media\/cache\/event_large\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_large_2x":"https:\/\/events.umich.edu\/media\/cache\/event_large_2x\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/event_large_lightbox\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "group_thumb":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_thumb\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "group_thumb_square":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_thumb_square\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "group_large":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_large\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "group_large_lightbox":"https:\/\/events.umich.edu\/media\/cache\/resolve\/group_large_lightbox\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_large_crop":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_large_crop\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_list":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_list\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_list_2x":"https:\/\/events.umich.edu\/media\/cache\/event_list_2x\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_grid":"https:\/\/events.umich.edu\/media\/cache\/event_grid\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_grid_2x":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_grid_2x\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_feature_large":"https:\/\/events.umich.edu\/media\/cache\/resolve\/event_feature_large\/media\/attachments\/2026\/01\/event_143254_original-1.jpg", "event_feature_thumb":"https:\/\/events.umich.edu\/media\/cache\/event_feature_thumb\/media\/attachments\/2026\/01\/event_143254_original-1.jpg" }, "occurrence_count":1, "first_occurrence":21892556 } , "143053-21891986": { "datetime_modified":"20260116T060134", "datetime_start":"20260116T100000", "datetime_end":"20260116T160000", "has_end_time":1, "date_start":"2026-01-16", "date_end":"2026-01-16", "time_start":"10:00:00", "time_end":"16:00:00", "time_zone":"America\/Detroit", "event_title":"Woven Horizons - 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