October 26, 2017
Govinda Anantha Padmanabha, Souvik Chakraborty, Shaoxing Mo
October 19, 2017
Souvik Chakraborty, Nick Geneva
October 13, 2017
Souvik Chakraborty, Navid Shervani-Tabar, Yinhao Zhu
Approximate inference algorithms [Slides]
Convolutional Neural Networks [Slides]
Intro to Stein's method, with applications to Bayesian Surrogate Modeling [Slides]
Stein's method  is a theoretical technique to obtain boun...
October 6, 2017
Steven Atkinson, Govinda Anantha Padmanabha
Parallel approaches for the Bayesian Gaussian process latent variable model [Slides]
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...
September 29, 2017
Souvik Chakraborty, Shaoxing Mo, Sina Malakpour, Govinda Anatha Padmanabha
Review of state space models [Slides]
Introduction to simulation models for groundwater flow modeling [Slides]
Cluster Expansion and Introduction to Alloy Theoretic Automated Toolkit [Slides]
Regularization in optimization [Slide...
September 22, 2017
Shaoxing Mo, Souvik Chakraborty
An adaptive experimental design for Global sensitivity analysis (GSA) and uncertainty quantification (UQ): Application to groundwater modeling [Slides]
Global sensitivity analysis (GSA) and uncertainty quantification (UQ) in groundwater modeling are challengi...
September 14, 2017
Govinda Anantha Padmanabha, Souvik Chakraborty
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...