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
Generalization to local remappings of the visuomotor coordinate transformation.
TL;DR: A simple model, in which the transformation is computed via the population activity of a set of units with large sensory receptive fields, is shown to capture the observed pattern.
Proceedings Article
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes
TL;DR: This work develops a statistical framework for the simultaneous, unsupervised segmentation and discovery of visual object categories from image databases, and uses Gaussian processes to discover spatially contiguous segments which respect image boundaries.
Proceedings Article
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers
TL;DR: Transferable Adversarial Training (TAT) is proposed to enable the adaptation of deep classifiers and advances the state of the arts on a variety of domain adaptation tasks in vision and NLP, including object recognition, learning from synthetic to real data, and sentiment classification.
Posted Content
Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning.
TL;DR: By enabling wider use of learned dynamics models within a model-free reinforcement learning algorithm, this work improves value estimation, which, in turn, reduces the sample complexity of learning.
Journal ArticleDOI
A kernel-based learning approach to ad hoc sensor network localization
TL;DR: It is shown that the coarse- grained and fine-grained localization problems for ad hoc sensor networks can be posed and solved as a pattern recognition problem using kernel methods from statistical learning theory, and a simple and effective localization algorithm is derived.