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Organizing Committee
Weizmann Institute of Science
University of North Carolina, Chapel Hill
University of Maryland
Abstract
Recent advances in machine learning have had a profound impact on computer vision. Simultaneously, success in computer vision applications has rapidly increased our understanding of some machine learning techniques, especially their applicability. This workshop will bring together researchers who are building a stronger theoretical understanding of the foundations of machine learning with computer vision researchers who are advancing our understanding of machine learning in practice.
Much of the recent growth in the use of machine learning in computer vision has been spurred by advances in deep neural networks. At the same time, new advances in other areas of machine learning, including reinforcement learning, generative models, and optimization methods, hold great promise for future impact. These raise important fundamental questions, such as understanding what influences the ability of learning algorithms to generalize, understanding what causes optimization in learning to converge to effective solutions, and understanding how to make optimization more efficient.
The workshop will include machine learning researchers who are addressing these foundational questions. It will also include computer vision researchers who are applying machine learning to a host of problems, such as visual categorization, 3D reconstruction, event and activity understanding, and semantic segmentation.
Much of the recent growth in the use of machine learning in computer vision has been spurred by advances in deep neural networks. At the same time, new advances in other areas of machine learning, including reinforcement learning, generative models, and optimization methods, hold great promise for future impact. These raise important fundamental questions, such as understanding what influences the ability of learning algorithms to generalize, understanding what causes optimization in learning to converge to effective solutions, and understanding how to make optimization more efficient.
The workshop will include machine learning researchers who are addressing these foundational questions. It will also include computer vision researchers who are applying machine learning to a host of problems, such as visual categorization, 3D reconstruction, event and activity understanding, and semantic segmentation.
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Lecture Videos
Karen Livescu
Toyota Technological Institute at Chicago
February 18, 2019 • 12:00 AM
Kate Saenko
Boston University
February 18, 2019 • 12:00 AM
Rob Fergus
Courant Institute of Mathematical Sciences, New York University
February 18, 2019 • 12:00 AM
Nadav Cohen
Institute for Advanced Study
February 19, 2019 • 12:00 AM
Rene Vidal
Johns Hopkins University
February 19, 2019 • 12:00 AM
Svetlana Lazebnik
University of Illinois, Urbana-Champaign
February 19, 2019 • 12:00 AM
