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. In this paper we propose a framework to systematically investigate bias and generalization in deep generative models of images. 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. 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 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. I work in the PAC group in Stanford Vision and Learning Lab under Dr. Juan Carlos Niebles and Prof. Fei-Fei Li. parity constraints are easier to solve but h...Join ResearchGate to find the people and research you need to help your work.Only verified researchers can join ResearchGate and send messages to other members.University students and faculty, institute members, and independent researchersTechnology or product developers, R&D specialists, and government or NGO employees in scientific rolesHealth care professionals, including clinical researchersJournalists, citizen scientists, or anyone interested in reading and discovering researchSorry, you need to be a researcher to join ResearchGate.Due to our privacy policy, only current members can send messages to people on ResearchGate.© 2008-2020 ResearchGate GmbH.
I earned a B.S. Short Tyson McMillan, Ph.D. Tyson McMillan, Ph.D. ODC pddl: PDDL Public Domain Dedication and License This is Russell. parity or XOR constraints. All rights reserved. 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. Research from Stanford University Data and More from Stanford's Cutting Edge Researchers. 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. Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate. provide strong theoretical guarantees but are computationally difficult. We propose an information-theoretically motivated objective for learning maximally expressive representations subject to fairness constraints. We identify the reason for this short-coming in the regularization term used in the ELBO criterion to match the variational...Advances in neural network based classifiers have transformed automatic feature learning from a pipe dream of stronger AI to a routine and expected property of practical systems. 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. Vanessa Neumann, London envoy of Venezuela's would-be president. 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. This undermines the purpose of unsupervised representation learning. 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 Inspired by experim...A variety of learning objectives have been proposed for training latent variable generative models. Despite recent empirical success, the inductive bias of deep generative models is not well understood.
Learning generative models for graph-structured data is challenging because graphs are discrete, combinatorial, and the underlying data distribution is invariant to the ordering of nodes. Since the emergence of AlexNet every winning submission of the ImageNet challenge has employed end-to-end representation learning, and due to the utility of good represent...We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. In...Partial differential equations (PDEs) are widely used across the physical and computational sciences. Many prior works require paired, aligned demonstrations and an additional RL procedure for the task.
Decades of research and engineering went into designing fast iterative solution methods. Stephen Sackur interviews Stuart Russell, a globally-renowned computer scientist and sometime adviser to the UK Government and the UN.JavaScript seems to be disabled. We...In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Please enable JavaScript to take full advantage of iPlayer.Stephen Sackur speaks to Armenia’s prime minister Nikol Pashinyan.Stephen Sackur speaks to Raoul Nehme, still Lebanon's minister of economy and trade.Chris Packham considers the impact of the Covid-19 pandemic on the natural world.Stephen Sackur speaks to Vanessa Neumann, London envoy of Venezuela's would-be president.STUART RUSSELL - Professor of Computer Science, University of California, Berkeley Nehme, still Lebanon's minister of economy and trade. Generative models, on the other hand, have benefited less from hierarchical models with multiple layers of latent variables. Home; ... Stewart, Russell Description: ... Topic: Stanford Department of Computer Science Collection: Stanford Research Data. The 2015 Stanford paper was authored by three University affiliates: Russell Stewart, Mykhaylo Andriluka and Andrew Ng. We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties. STUART RUSSELL - Professor of Computer Science, University of California, Berkeley.