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Title:
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Transferring Nonlinear Representations using Gaussian Processes with a Shared Latent Space |
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Author:
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Urtasun, Raquel; Quattoni, Ariadna; Lawrence, Neil; Darrell, Trevor |
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Other Contributors:
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Vision |
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Advisor:
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Trevor Darrell |
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Issue Date:
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2008-04-11 |
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Abstract:
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When a series of problems are related, representations derived from learning earlier tasks may be useful in solving later problems. In this paper we propose a novel approach to transfer learning with low-dimensional, non-linear latent spaces. We show how such representations can be jointly learned across multiple tasks in a Gaussian Process framework. When transferred to new tasks with relatively few training examples, learning can be faster and/or more accurate. Experiments on digit recognition and newsgroup classification tasks show significantly improved performance when compared to baseline performance with a representation derived from a semi-supervised learning approach or with a discriminative approach that uses only the target data. |
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URI:
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http://hdl.handle.net/1721.1/41517
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Other Identifiers:
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MIT-CSAIL-TR-2008-020 |
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Related To
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Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
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