2 code implementations • 5 Oct 2021 • James Martens, Andy Ballard, Guillaume Desjardins, Grzegorz Swirszcz, Valentin Dalibard, Jascha Sohl-Dickstein, Samuel S. Schoenholz
Using an extended and formalized version of the Q/C map analysis of Poole et al. (2016), along with Neural Tangent Kernel theory, we identify the main pathologies present in deep networks that prevent them from training fast and generalizing to unseen data, and show how these can be avoided by carefully controlling the "shape" of the network's initialization-time kernel function.
1 code implementation • 2 Oct 2019 • John F. J. Mellor, Eunbyung Park, Yaroslav Ganin, Igor Babuschkin, tejas kulkarni, Dan Rosenbaum, Andy Ballard, Theophane Weber, Oriol Vinyals, S. M. Ali Eslami
We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804. 01118).