Arthur Jacot

Congratulations to Arthur Jacot (NYU) on winning the $50,000 AMR @AMathRes Prize in the Mathematics of AI! amathr.org/prizes/aiprize… From the citation: "His most influential contribution to the subject is the development of the Neural Tangent Kernel framework. This work, introduced in 2018, provides a rigorous theoretical tool for understanding the behavior of overparameterized or “wide” deep neural networks trained by gradient descent. Jacot and his colleagues proved that as the width of a network approaches infinity, the network’s evolution during training becomes linear and deterministic, governed by a fixed, positive-definite kernel known as the Neural Tangent Kernel . This result shows that in this specific “lazy training” regime, the highly non-convex optimization problem of training a deep neural network can be reduced to a well-understood problem in classical kernel methods, allowing researchers to analytically predict generalization and convergence rates without relying on empirical simulations."

Very happy to be part of this project. We've compiled the main reasons why a Theory of Deep Learning is possible if not inevitable!

1/ Deep learning is going to have a scientific theory. We can see the pieces starting to come together, and it's looking a lot like physics! We're releasing a paper pulling together these emerging threads and giving them a name: learning mechanics. 🔨 arxiv.org/pdf/2604.21691 🔧