Student Brown Bag Seminar

This talk will explore the equivalence between neural networks and kernel methods (and reproducing kernel spaces more generally) by deriving a representation of any finite-size parametric classification model trained with gradient descent as a kernel machine. We will compare this exact representation to the well-known Neural Tangent Kernel (NTK) and discuss approximation accuracy. We will apply this kernel in several experimental contexts to demonstrate that this theoretical foundation informs cutting edge algorithms on several machine-learning tasks. Last, we will discuss several natural applications of this theory which we are only just beginning to explore.