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Approximate inference algorithms [Slides]

Souvik Chakraborty

Convolutional Neural Networks [Slides]

Navid Shervani-Tabar

Intro to Stein's method, with applications to Bayesian Surrogate Modeling [Slides]

Yinhao Zhu

Stein's method [1] is a theoretical technique to obtain boun...

Parallel approaches for the Bayesian Gaussian process latent variable model [Slides]

Steven Atkinson

We consider the task of training a Bayesian GP-LVM and uncover opportunities to employ parallelism in computing the collapsed lower bound to the model evidence and its gr...

Review of state space models [Slides]

Souvik Chakraborty

Introduction to simulation models for groundwater flow modeling [Slides]

Shaoxing Mo

Cluster Expansion and Introduction to Alloy Theoretic Automated Toolkit [Slides]

Sina Malakpour

Regularization in optimization [Slide...

An adaptive experimental design for Global sensitivity analysis (GSA) and uncertainty quantification (UQ): Application to groundwater modeling [Slides]

Shaoxing Mo

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) in groundwater modeling are challengi...

Regularization in Deep Learning [Slides]

Govinda Anantha Padmanabha

Regularization is any modification we make to a learning algorithm that is intended to reduce its test error but not training error. This seminar reviews on regularization strategies for deep models or m...