M
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.
Papers
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Journal ArticleDOI
Transferable Representation Learning with Deep Adaptation Networks
TL;DR: A novel framework for deep adaptation networks is developed that extends deep convolutional neural networks to domain adaptation problems and yields state-of-the-art results on standard visual domain-adaptation benchmarks.
Journal ArticleDOI
Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study
TL;DR: The effects of artificial visual feedback on planar two-joint arm movements are studied to suggest that spatial perception-as mediated by vision-plays a fundamental role in trajectory planning and suggests that trajectories are planned in visually based kinematic coordinates.
Proceedings ArticleDOI
Failure diagnosis using decision trees
TL;DR: A decision tree learning approach to diagnosing failures in large Internet sites is presented, and it is found that, among hundreds of potential causes, the algorithm successfully identifies 13 out of 14 true causes of failure, along with 2 false positives.
Book
Feedforward Neural Network Methodology
TL;DR: This monograph provides a through and coherent introduction to the mathematical properties of feedforward neural networks and to the computationally intensive methodology that has enabled their highly successful application to complex problems of pattern classification, forecasting, regression, and nonlinear systems modeling.
Proceedings Article
MLbase: A Distributed Machine-learning System
Tim Kraska,Ameet Talwalkar,John C. Duchi,Rean Griffith,Michael J. Franklin,Michael I. Jordan +5 more
TL;DR: This work presents the vision for MLbase, a novel system harnessing the power of machine learning for both end-users and ML researchers, which provides a simple declarative way to specify ML tasks and a novel optimizer to select and dynamically adapt the choice of learning algorithm.