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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...

September 7, 2017

Implementation of Neural Nets in PyTorch [Slides] 

Nick Geneva

In this seminar we review and discuss the implementation of two different neural nets. First we discuss using mixture density networks to fit Gaussian distributions to a set of toy data and implementing a cus...

Deep Feedforward Networks [Slides]
Navid Shervani-Tabar
 
We review the deep feedforward networks. General setup and design decisions would be discussed - choosing the optimizer, the cost-function, and the form of the output units. We review the basics of gradient-base...

August 24, 2017

We review and discuss the structure and implementation of basic neural networks using PyTorch. Polynomial fitting, classification, and mixture density networks will be discussed along with coding details for replications of results found in the literature.

[Slides]

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