August - 2017

Deep Feedforward Networks, Bayesian Gaussian Process Latent Variable Model

Deep Feedforward Networks [Slides] Navid Shervani-Tabar We review the deep feedforward networks. General setup and design decisions...

Implementation of Neural networks using PyTorch

We review and discuss the structure and implementation of basic neural networks using PyTorch. Polynomial fitting, classification, and...

Sparse Gaussian Processes

We review the motivation for and implementation of sparse Gaussian processes. Special attention is given to the variational method of...

Expectation Propagation, Model uncertainty in RANS simulation, Variational Auto-Encoders

Introduction to Expectation Propagation [Slides] Souvik Chakraborty Expectation propagation (EP) is an approximate Bayesian inference...

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AI Hardware DURIP Grant Awarded

Boltzmann Machine; Undirected Graphical Models; Generative Models in Geology

Undirected Graphical Models; Deep Learning for Fluid Mechanics

Approximate Inference; Convolutional Neural Networks; Stein's Method

Parallel approaches for the Bayesian Gaussian process latent variable model; Regularization in optim

Simulation models for groundwater flow modeling; Cluster Expansion and Introduction to Alloy Theoret

UQ in Groundwater Modeling, State Space Models

A Review of Markov and hidden Markov Model, Regularization in Deep Learning

Implementation of Neural Nets in PyTorch

Deep Feedforward Networks, Bayesian Gaussian Process Latent Variable Model

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