Randall Balestriero

Randall Balestriero, an assistant professor at Brown University specializing in representation learning, dives deep into Joint Embedding Predictive Architectures (JEPA). He explains how JEPA learns data representations without reconstruction, focusing on meaningful features while compressing irrelevant details. The discussion covers the challenges of model collapse, prediction tasks shaping feature learning, and the implications for AGI benchmarks. Balestriero also shares insights on evaluating JEPA models, the role of latent variables, and the growing opportunity in JEPA research.