Our guest speaker is Dr. Shuai Zhang from the department of computer science at ETH Zurich and you are all cordially invited to the CVG Seminar on April 30th at 2:30 p.m. CET on Zoom (passcode is 765585), where‪ Shuai will give a talk titled “Uncovering the Intrinsic Structures: Representation Learning and Its Applications“.

Abstract

Learning suitable data representation lives at the heart of many intelligent applications. The quality of the learned representations is determined by how well the model uncovers the intrinsic structures of data.
In this talk, I will first describe our recent work on geometry-oriented representation learning and demonstrate how applications that heavily rely on representation learning can benefit from it. In particular, I will present a data-driven approach, switch space, a novel way of combining spherical, euclidean, and hyperbolic spaces in a single model with specialization. Using switch spaces, we obtain state-of-the-art performances on knowledge graph completion and recommender systems.  Then, I will introduce our ICLR 2021 work on learning representations in hypercomplex space, including the parameterized hypercomplex multiplication layer and its applications on LSTM and Transformer.

Bio

Shuai Zhang is a postdoctoral researcher in the department of computer science at ETH Zurich, where he works with Prof. Ce Zhang. He received his Ph.D. in computer science from the University of New South Wales, under the supervision of Prof. Lina Yao.  His current research lies in geometry-oriented representation learning and its applications in information filtering, knowledge graph completion, and reasoning. He is a recipient of the outstanding paper award at ICLR 2021 and the best paper award runner-up at WSDM 2020.