Room 327

Special lecture in the Machine Learning Course: Deep neural circuits

Anirbit Mukherjee (Johns Hopkins University, USA)

Johns Hopkins University, USA

"Deep Learning"/"Deep Neural Nets" is a technological marvel in our quest towards artificial intelligence and is now increasingly widely used. It has been shown to have unprecedented performance for a wide variety of purposes from playing chess to self-driving cars to astrophysics to experimental high-energy physics. But this recent revolutionary practical success of deep neural nets has turned out to be extremely challenging to be explainable by any theoretical framework.

In this review talk I will start from the basics and give an overview of the theorems that we have proven in our attempts at making sense of deep learning. I will survey our work about (a) depth-hierarchy theorems for deep neural circuits, (b) convergence of algorithms which optimize on these function spaces and (c) theoretical methods to estimate the risk function of neural nets.