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Weekly laboratory research seminars on research project development and critical review of literature in the interface of machine learning, deep learning and scientific computing.
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
Implementation of Neural networks using PyTorch
Sparse Gaussian Processes
Expectation Propagation, Model uncertainty in RANS simulation, Variational Auto-Encoders