In...Partial differential equations (PDEs) are widely used across the physical and computational sciences. provide strong theoretical guarantees but are computationally difficult. 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 We...In high dimensional settings, density estimation algorithms rely crucially on their inductive bias. Short 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. STUART RUSSELL - Professor of Computer Science, University of California, Berkeley. 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 We propose an information-theoretically motivated objective for learning maximally expressive representations subject to fairness constraints. Inspired by experim...A variety of learning objectives have been proposed for training latent variable generative models. This is Russell. 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. Long parity constraints (involving many variables) We propose A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to produce samples with desired properties. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor. I earned a B.S. 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. 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. I am a graduate student in Computer Science at Stanford, specialized in AI. All rights reserved.

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. 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. Tyson McMillan, Ph.D. Tyson McMillan, Ph.D. 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. The 2015 Stanford paper was authored by three University affiliates: Russell Stewart, Mykhaylo Andriluka and Andrew Ng. 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.
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. 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. 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. In this paper we propose a framework to systematically investigate bias and generalization in deep generative models of images. Generative models, on the other hand, have benefited less from hierarchical models with multiple layers of latent variables. Despite recent empirical success, the inductive bias of deep generative models is not well understood. In contrast to existing hand-crafted solutions, we propose an ap...Learning data representations that are transferable and fair with respect to certain protected attributes is crucial to reducing unfair decisions made downstream, while preserving the utility of the data. However, paired, aligned demonstrations are seldom obtainable and RL procedures are expensive. 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. 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.

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