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This page maintains the seminar presentations of the Bayesian Machine Learning group supervised by Dr. Shandian Zhe at the University of Utah.

Archived

Graph Diffusion
Date: Dec 12 2024, Presenter: Zak Bastiani
video

Anomaly detection and segmentation via deep learning
Date: Dec 11 2024, Presenter: Gaurav
slides | video1 | video2

Diffusion model and Inverse Problem
Date: Feb 21 2024, Presenter: ZhiTong Xu
slides | video

How to use Sequential Monte Carlo for optimization
Date: Nov 22 2023, Presenter: Su-wei Yang
slides | video

Large Language Models Quantization (Part 2)
Date: Nov 22 2023, Presenter: Xin Yu
slides | video1 | video2

How to use Sequential Monte Carlo for optimization
Date: Nov 3 2023, Presenter: Haozhe Sun
slides

Large Language Models Quantization (Part 1)
Date: Nov 1 2023, Presenter: Xin Yu
slides | video

Recent papers in hypergraph
Date: Oct 18 2023, Presenter: Sina Rashetnia
slides | video1 | video2

A General Neural Operator Transformer for Operator Learning
Date: Sep 13 2023, Presenter: Tushar Gautam
slides | video

Kernel Integral Operator by Wavelet
Date: Sep 13 2023, Presenter: Su-Wei Yang
slides | video

HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
Date: Sep 6 2023, Presenter: Keyan Chen
slides | video1 | video2

High-Dimensional Bayesian Optimization via Random Projection
Date: Sep 6 2023, Presenter: Zhitong Xu
slides | video

Sequential Monte-Carlo
Date: Aug 30 2023, Presenter: Haozhe Sun
slides | video

Data-driven Methods for Atomosphere Research
Date: Aug 30 2023, Presenter: Wenlin Li
slides 1 | slides 2 | video

A Continuous Analogue of the Tensor-Train Decomposition
Date: Jun 5th 2023, Presenter: Yile Li
slides

From Diffusion Model to Schrodinger Bridge
Date: Aug 31 2022, Presenter: Shikai Fang
slides | video part1 | video part2

A Deterministic Streaming Sketch for Ridge Regression
Date: Mar 18 2022, Presenter: Xin Yu
slides | video

ODE on Graph: A Diffusion Example
Date: Mar 16 2022, Presenter: Shikai Fang
slides | video

DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
Date: Mar 4 2022, Presenter: Dillon Lee
slides | video

DeepONet and Physics-Informed Operator Learning
Date: Mar 2 2022, Presenter: Shibo Li
slides | video

Scalable Gradients for Stochastic Differential Equations
Date: Feb 28 2022, Presenter: Shikai Fang
slides | video

Physics-Informed Learning of Governing Equations from Scarce Data
Date: Feb 25 2022, Presenter: Zheng Wang
slides | video

Elliptical Slice Sampling
Date: Feb 23 2022, Presenter: Da Long
slides | video

Probabilistic Network Pruning
Date: Feb 21 2022, Presenter: Xin Yu
slides | video

Dirichlet Pruning for Neural Network Compression
Date: Feb 18 2022, Presenter: Xin Yu
slides | video

Multiresolution Gaussian Processes
Date: Feb 16 2022, Presenter: Zheng Wang
slides | video

Neural Operator: Learning Maps Between Function Spaces
Date: Feb 14 2022, Presenter: Shibo Li
slides | video part1 | video part2

Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Date: Feb 11 2022, Presenter: Nancy Lyu
slides | video

Neural Tangent Kernel and PINNs
Date: Feb 9 2022, Presenter: Madison Cooley
slides | video

Fourier Transform, Time Seriers and Point Process
Date: Nov 8 2021, Presenter: Zheng Wang
slides

Multi-Armed Bandits: An Introduction
Date: Nov 2 2021, Presenter: Shibo Li
slides

Model Selection in Bayesian Neural Networks via Horseshoe Priors
Date: Oct 26 2021, Presenter: Da Long
slides

Introduction to Kalman-Filter, SDE and LFM
Date: Oct 26 2021, Presenter: Shikai Fang
slides

Bayesian Optimization with Finite Budget
Date: Sep 14 2021, Presenter: Shibo Li
slides

Introduction to Attention
Date: Sep 14 2021, Presenter: Shikai Fang
slides

A Brief Introduction to Graph Neural Networks
Date: Sep 8 2021, Presenter: Zheng Wang
slides

Literature Review: Network Compression
Date: Sep 1 2021, Presenter: Xin Yu
slides

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