March 7, 2023 Tuesday
Sparsity is commonly produced from model compression (i.e., pruning), which eliminates unnecessary parameters in deep neural networks (NN) for efficiency purposes. However, its significance extends beyond this, as sparsity has been exploited to model the underlying low dimensionality of neural networks, and to understand their implicit regularization, generalization, and robustness. My work demonstrates that learning with sparsity-aware priors substantially improves performances through a full stack of applied work on algorithms, systems, and scientific challenges. In this talk, I will start from (1) efficient sparse models by presenting a special kind of sparse NN which is capable of universally transferring across diverse downstream tasks and matches the full accuracy of its dense counterpart; then I will share more insights about (2) sparsity beyond efficiency, including boosted generalization and robustness from sparsity-regularized training; in the end, I will describe what is the prospect of (3) sparsity for science such as COVID-19 vaccine design and quantum computing.
Mr. Tianlong CHEN
PhD Candidate, Electrical and Computing Engineering department, University of Texas at Austin
Tianlong Chen is a final-year Ph.D. candidate in the Electrical and Computing Engineering department at the University of Texas at Austin. His research focuses on building accurate and responsible machine learning (ML) systems. He devotes his most recent passion to learning with sparsity – which tightly connects to various important topics including ML model efficiency, reliability, learning to optimize, and interdisciplinary scientific challenges such as bioengineering and quantum computing. He has co-authored over 90 papers at top-tier venues (NeurIPS, ICML, ICLR, JMLR, CVPR, ICCV, ECCV, TPAMI, etc.), including a Best Paper Award at LoG’22. Tianlong is a recipient of the IBM Ph.D. Fellowship Award, Adobe Ph.D. Fellowship Award, and Graduated Dean’s Prestigious Fellowship.