Explorations at the Physics ∩ ML Interface
Kyle Cranmer, NYU, 12:00 ETAbstract: Instead of focusing on a specific application, I will discuss a few projects that explore the Physics ∩ ML Interface. How do we incorporate our physical insight into the underlying causal mechanism into the inductive bias of machine learning architectures? Is that helpful or necessary? Why do we care if a model is interpretable? Where do we stand on the spectrum between ML-supercharged data analysis and an AI / robot scientist? How does this line of thinking influence research in AI and ML?