图 书 馆
高效离散优化问题结构的开发Exploiting Problem Structure for Efficient Discrete Optimization |
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| 课程网址: | http://videolectures.net/nipsworkshops2011_kohli_optimization/ |
| 主讲教师: | Pushmeet Kohli |
| 开课单位: | 微软公司 |
| 开课时间: | 2012-01-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Many problems in computer vision and machine learning require inferring the most probable states of certain hidden or unobserved variables. This inference problem can be formulated in terms of minimizing a function of discrete variables. The scale and form of computer vision problems raise many challenges in this optimization task. For instance, functions encountered in vision may involve millions or sometimes even billions of variables. Furthermore, the functions may contain terms that encode very high-order interaction between variables. These properties ensure that the minimization of such functions using conventional algorithms is extremely computationally expensive. In this talk, I will discuss how many of these challenges can be overcome by exploiting the sparse and heterogeneous nature of discrete optimization problems encountered in real world computer vision problems. Such problem-aware approaches to optimization can lead to substantial improvements in running time and allow us to produce good solutions to many important problems. |
| 关 键 词: | 计算机视觉; 机器学习; 离散变量; 函数最小化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-07：chenxin |
| 阅读次数: | 24 |