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Michael I. Jordan
Researcher at University of California, Berkeley
Publications - 1110
Citations - 241763
Michael I. Jordan is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Inference. The author has an hindex of 176, co-authored 1016 publications receiving 216204 citations. Previous affiliations of Michael I. Jordan include Stanford University & Princeton University.
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Experience mining Google's production console logs
TL;DR: The early experience shows that the techniques, including source code based log parsing, state and sequence based feature creation and problem detection, work well on this production data set.
Variational inference in graphical models: The view from the marginal polytope
TL;DR: Taking the “zero-temperature limit” recovers a variational representation for MAP computation as a linear program (LP) over the marginal polytope, which clarifies the essential ingredients of known variational methods, and also suggests novel relaxations.
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Active Learning for Crowd-Sourced Databases
TL;DR: Two new active learning algorithms are presented to combine humans and algorithms together in a crowd-sourced database, based on the theory of non-parametric bootstrap, which makes their results applicable to a broad class of machine learning models.
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MultiVI: deep generative model for the integration of multi-modal data
TL;DR: MultiVI as discussed by the authors is a probabilistic framework that leverages deep neural networks to jointly analyze scRNA, scATAC and multiomic (scRNA+scATAC) data.
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HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
TL;DR: HopSkipJumpAttack is developed, a family of algorithms based on a novel estimate of the gradient direction using binary information at the decision boundary that achieves competitive performance in attacking several widely-used defense mechanisms.