NSFC-62206101: Fundamental Limits of Pruning Deep Neural Network Models via Random Matrix Methods

This project (2023.01-2025.12) is led by myself, and focuses on the fundamental theoretical limits of pruning as well as quantization of deep neural networks. The objective of this project is to propose, by developing the mathematical tools of random matrix theory, high-dimensional statistics, and optimization theory, a quantitative theory to characterize the “performance and complexity tradeoff” in modern deep neural nets.

CCF-Hikvision Open Fund 20210008: Random Matrix Theory and Information Bottleneck for Neural Network Compression

This project is led by Prof. Kai Wan and myself as PI, and investigates efficient compression schemes of large-scale neural network models with strong theoretical guarantees. The project leads to the following scientific publications:

  1. H. Tiomoko, Z. Liao, R. Couillet, “Random matrices in service of ML footprint: ternary random features with no performance loss”, The Tenth International Conference on Learning Representations (ICLR'2022), 2022. preprint

  2. L. Gu, Y. Du, Y. Zhang, D. Xie, S. Pu, R. C. Qiu, Z. Liao, “Lossless Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach”, 2022.

See more details of the project in Chinese here.

NSFC-12141107: Mathematical theory and methods for Reconfigurable Intelligent Surface (RIS) assisted wireless communication

This project (2022.01-2025.12) is led by Prof. R. C. Qiu and investigates the (information) theoretical limits of RIS assisted wireless communication system, dynamical system, as well as the theory of large dimensional random matrices.

See the project homepage here.