Join us for talks from new faculty on emerging AI topics, including optimizing neural nets, large language models, and generative AI for 3D scenes.
- 10:00-10:30am Check-in
- 10:30-10:35am Opening Remarks
- 10:35-11:00am "Explorations of Optimization and Generalization for Neural Nets"
- 11:00-11:25am "An Adventure Inside a Large Language Model"
- 11:25-11:50am "Generative Models in Three Dimensions"
- 11:50-12:15pm Q&A panel with all speakers
Micah Goldblum:
"Explorations of Optimization and Generalization for Neural Nets"
The fundamental building blocks of machine learning are (1) optimization or fitting the training data and (2) generalization or extrapolating from the training data to unseen samples. In this talk, we will explore both of these topics in the context of neural networks. First, we will discuss recent work on stabilizing optimization for language models. We show that vanilla SGD without momentum is nearly as fast as Adam in the small-batch regime. Then, we will discuss work on how to build neural networks that can generalize to far more complex problems than they were trained on by “thinking” for longer.