图 书 馆
排列分布有效推理的自适应表示Adaptive Representations for Efficient Inference for Distributions on Permutations |
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| 课程网址: | http://videolectures.net/ripd07_guestrin_are/ |
| 主讲教师: | Carlos Guestrin |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2008-02-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Permutations are ubiquitous in many real world problems, such as voting, rankings and data association. Representing uncertainty over permutations is challenging, since there are $n!$ possibilities, and typical compact representations, such as graphical models, cannot efficiently capture the mutual exclusivity constraints associated with permutations. In this talk, we use the ''low-frequency'' terms of a Fourier decomposition to represent such distributions compactly. We first describe how the two standard probabilistic inference operations, conditioning and marginalization, can be performed entirely in the Fourier domain in terms of these low frequency components, without ever enumeration $n!$ terms. We also describe a novel approach for adaptively picking the complexity of this representation in order control the resulting approximation error. We demonstrate the effectiveness of our approach on a real camera-based multi-people tracking setting. This presentation is joint work with Jon Huang and Leo Guibas. |
| 关 键 词: | 排列; 图形模型; 多人跟踪设置 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-22：chenxin |
| 阅读次数: | 37 |