We use theoretical and empirical approaches to build machine vision systems that see and understand the world like humans.
Investigating the interplay between optimizer and architecture in Deep Learning, and new networks for long-range reasoning.
Building theoretical and practical tools to support responsible and reliable machine learning in social context.
Inference performed on the basis of empirical data.
Investigating the feasibility of technical solutions to safety and efficiency in machine learning.