Stephen Sackur interviews Stuart Russell, a globally-renowned computer … Hi there! We show that many of them, including InfoGAN, ALI/BiGAN, ALICE, CycleGAN, beta-VAE, adversarial autoencoders, AVB, AS-VAE and InfoVAE, are Lagrangian duals of the same primal optimization problem, corresponding to different settings of the Lagrange m...The variational autoencoder (VAE) is a popular model for density estimation and representation learning. Our foundation is based on a variational extension of Shannon's information theory that takes into account the modeling power and computational constraints of the observer. I work in the PAC group in Stanford Vision and Learning Lab under Dr. Juan Carlos Niebles and Prof. Fei-Fei Li. Generative models, on the other hand, have benefited less from hierarchical models with multiple layers of latent variables.

In...Partial differential equations (PDEs) are widely used across the physical and computational sciences. The resulting \emph{predictive $\mathcal{V}$-information} encompasses mutual information and other...Generative models have made immense progress in recent years, particularly in their ability to generate high quality images. We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties. I am a graduate student in Computer Science at Stanford, specialized in AI. impact of the Covid-19 pandemic on the natural world. We find that existing training objectives for variational autoencoders can lead to inaccurate amortized inference distributions and, in some cases, improving the objective provably degrades the inference quality. We provide conditions under which they recover the data distribution and learn latent features, and formally show that common issues such as blurry samples and uninformative latent features arise when these conditio...Deep neural networks have been shown to be very successful at learning feature hierarchies in supervised learning tasks. We...In high dimensional settings, density estimation algorithms rely crucially on their inductive bias.

Decades of research and engineering went into designing fast iterative solution methods. Short STUART RUSSELL - Professor of Computer Science, University of California, Berkeley. Stuart Russell Professor of Computer Science and Smith-Zadeh Professor in Engineering, University of California, Berkeley and Honorary Fellow, Wadham College, Oxford Mailing address: Computer Science Division 387 Soda Hall University of California Berkeley, CA 94720-1776 San Francisco Bay Area. Tyson McMillan, Ph.D. Tyson McMillan, Ph.D. However, most of the existing generative models for graphs are not invariant to the chosen ordering, which might lead to an undesirable bias in the learned distri...We propose a new framework for reasoning about information in complex systems. This undermines the purpose of unsupervised representation learning. All rights reserved. Professor of Computer Science at Stanford University. In this paper, we prove that certain classes of hierarchical latent variable models do not take advantag...Many recent algorithms for approximate model counting are based on a ODC pddl: PDDL Public Domain Dedication and License First, we...It has been previously observed that variational autoencoders tend to ignore the latent code when combined with a decoding distribution that is too flexible. Inspired by experim...A variety of learning objectives have been proposed for training latent variable generative models. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text.