Copyright © 2017 University of Notre Dame

CSE Laboratory 372 Fitzpatrick Hall, Notre Dame, IN 46556, USA

Phone 574-631-2429   nzabaras@nd.edu

Contact us

Accessibility Information

Please reload

Recent Posts

Our new website is launched today.

March 14, 2017

1/1
Please reload

Featured Posts

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

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 gradients with respect to its variational parameters.  We will discuss its implementation within a C++ code for deep GPs.

 

Reference:

Gal, Yarin, Mark van der Wilk, and Carl Edward Rasmussen. "Distributed variational inference in sparse Gaussian process regression and latent variable models." Advances in Neural Information Processing Systems. 2014.

 

 

Regularization in optimization [Slides]

Govinda Anantha Padmanabha

 

Tags:

Share on Twitter
Please reload

Please reload

Search By Tags
Please reload