Deep Generative Models and Biological Applications

Loading...
Thumbnail Image

Date

2017

Authors

Journal Title

Journal ISSN

Volume Title

Repository Usage Stats

252
views
1140
downloads

Abstract

High-dimensional probability distributions are important objects in a wide variety of applications.

Generative models provide an excellent manipulation method for training from rich available unlabeled data set and sampling new data points from underlying high-dimensional probability distributions.

The recent proposed Variational auto-encoders (VAE) framework is an efficient high-dimensional inference method to modeling complicated data manifold in an approximate Bayesian way, i.e., variational inference.

We first discuss how to design fast stochastic backpropagation algorithm for the VAE based amortized variational inference method.

Particularly, we propose second order Hessian-free optimization method for Gaussian latent variable models and provide a theoretical justification to the convergence of Monte Carlo estimation in our algorithm.

Then, we apply the amortized variational inference to a dynamic modeling application in flu diffusion task.

Compared with traditional approximate Gibbs sampling algorithm, we make less assumption to the infection rate.

Differing from the maximum likelihood approach of VAE, Generative Adversarial Networks (GAN) is trying to solve the generation problem from a game theoretical way.

From this viewpoint, we design a framework VAE+GAN, by placing a discriminator on top of auto-encoders based model and introducing an extra adversarial loss.

The adversarial training induced by the classification loss is to make the discriminator believe the sample from the generative model is as real as the one from the true dataset.

This trick can practically improve the quality of generation samples, demonstrated on images and text domains with elaborately designed architectures.

Additionally, we validate the importance of generative adversarial loss with the conditional generative model in two biological applications: approximate Turing pattern PDEs generation in synthetic/system biology, and automatic cardiovascular disease detection in medical imaging processing.

Description

Provenance

Citation

Citation

Fan, Kai (2017). Deep Generative Models and Biological Applications. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/16785.

Collections


Except where otherwise noted, student scholarship that was shared on DukeSpace after 2009 is made available to the public under a Creative Commons Attribution / Non-commercial / No derivatives (CC-BY-NC-ND) license. All rights in student work shared on DukeSpace before 2009 remain with the author and/or their designee, whose permission may be required for reuse.