Understanding, quantifying, and controlling learned representations of deep neural networks.

Deep learning models have become widely established, yet what they internally learn remains poorly understood. This disputation investigates their learned, internal representations through representational similarity analysis. It is shown that independent models converge to similar solutions, that their similarity can be better quantified, and that learned representations can be deliberately steered to different solutions. Finally, open problems in how such representations can be meaningfully compared are discussed.