主办单位：陕西师范大学数学与统计学院

报告内容：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, domain adaptation.

报告人简介：孙剑，西安交通大学数学与统计学院教授、博士生导师，国家杰出青年科学基金获得者，陕西省自然科学奖一等奖获得者。长期致力于图像分析与人工智能的数学基础与算法的研究。主要研究成果发表于IJCV, MIA, IEEE TIP, CVPR, NIPS, MICCAI等领域内著名国际期刊和会议，并担任权威国际期刊IJCV编委，重要国际会议ICCV/ECCV/MICCAI领域主席。曾在微软亚洲研究院、美国中佛罗里达大学、法国巴黎高师法国国家信息与自动化研究院等做博士后或访问学者。