Approximation Theory of Deep Learning for Sequence Modelling
报告人：Qianxiao Li（National University of Singapore）
地点：Room 1303, Sciences Building No. 1 【线上】Tencent ID: 227-124-893
In this talk, we present some recent results on the approximation theory of deep learning architectures for sequence modelling. In particular, we formulate a basic mathematical framework, under which different popular architectures such as recurrent neural networks, dilated convolutional networks (e.g. WaveNet), encoder-decoder structures, and most recently - transformers - can be rigorously compared. These analyses reveal some interesting connections between approximation, memory, sparsity/low-rank, graphical structures that may guide the practical selection and design of these network architectures.
Qianxiao Li is an assistant professor in the Department of Mathematics, and a principal investigator in the Institute for Functional Intelligent Materials, National University of Singapore. He graduated with a BA in mathematics from the University of Cambridge and a PhD in applied mathematics from Princeton University. His research interests include the interplay of machine learning and dynamical systems, control theory, stochastic optimisation algorithms and data-driven methods for science and engineering.