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Explores socially-aware AI for last-mile mobility, focusing on understanding social etiquettes, anticipating behaviors, and forecasting crowd movements.
Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.
Delves into the spectral bias of polynomial neural networks, analyzing the impact on learning different frequencies and discussing experimental results.
Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.
Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.
Introduces a functional framework for deep neural networks with adaptive piecewise-linear splines, focusing on biomedical image reconstruction and the challenges of deep splines.