图 书 馆
为高维学习扩展最优传输Scaling Optimal Transport for High dimensional Learning |
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| 课程网址: | http://videolectures.net/8ecm2021_peyre_scaling_transport/ |
| 主讲教师: | Gabriel Peyré |
| 开课单位: | 国家科学研究中心 (CNRS) |
| 开课时间: | 2021-07-06 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Optimal transport (OT) has recently gained lot of interest in machine learning. It is a natural tool to compare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is however plagued by the curse of dimensionality, since it might require a number of samples which grows exponentially with the dimension. In this talk, I will explain how to leverage entropic regularization methods to define computationally efficient loss functions, approximating OT with a better sample complexity. More information and references can be found on the website of our book “Computational Optimal Transport”. |
| 关 键 词: | 最佳运输; 几何损失函数; 熵正则化 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-03-27：hqh |
| 最后编审: | 2022-03-27：hqh |
| 阅读次数: | 34 |