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孙剑 教授: Deep Learning in Non-Euclidean Space

本站小编 Free考研考试/2021-12-26


Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars
Speaker: 孙剑 教授 ，西安交通大学数学与统计学院
Inviter: 陈冲 副研究员
Title:
 Deep Learning in Non-Euclidean Space
Time & Venue:
 2021.10.18 15:00-16:30 腾讯会议ID：779 919 771 入会密码：311311
Abstract:
 会议链接：https://meeting.tencent.com/dm/nkts0pJkwVNo
The traditional deep networks are commonly defined in Euclidean space, either in the 3D / 2D image space or sequential data space. However, in realistic scenario, the data maybe irregular or distributed on manifold / graph. In such cases, the traditional deep network does not fully take advantages of the underlying data structure in non-Euclidean space. Along this research direction, in this talk, I will introduce the research backgrounds, advances in research on geometric deep learning approach in the non-Euclidean space, with applications to 3D object recognition, image segmentation and domain adaptation.

相关话题/副研究员 统计学院 数学 西安交通大学 会议

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