Approximate Inference; Convolutional Neural Networks; Stein's Method
Approximate inference algorithms [Slides] Souvik Chakraborty Convolutional Neural Networks [Slides] Navid Shervani-Tabar Intro to Stein's...
Parallel approaches for the Bayesian Gaussian process latent variable model; Regularization in optim
Parallel approaches for the Bayesian Gaussian process latent variable model [Slides] Steven Atkinson We consider the task of training a...
Simulation models for groundwater flow modeling; Cluster Expansion and Introduction to Alloy Theoret
Review of state space models [Slides] Souvik Chakraborty Introduction to simulation models for groundwater flow modeling [Slides]...
UQ in Groundwater Modeling, State Space Models
An adaptive experimental design for Global sensitivity analysis (GSA) and uncertainty quantification (UQ): Application to groundwater...
A Review of Markov and hidden Markov Model, Regularization in Deep Learning
Regularization in Deep Learning [Slides] Govinda Anantha Padmanabha Regularization is any modification we make to a learning algorithm...
Implementation of Neural Nets in PyTorch
Implementation of Neural Nets in PyTorch [Slides] Nick Geneva In this seminar we review and discuss the implementation of two different...
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...
