384 citations · 388 across the 3 of their papers we have counts for
3 papers
A Brief Study of In-Domain Transfer and Learning from Fewer Samples using A Few Simple Priors
Marc Pickett, Ayush Sekhari, James Davidson
Domain knowledge can often be encoded in the structure of a network, such as convolutional layers for vision, which has been shown to increase generalization and decrease sample co…
Learning Hierarchical Information Flow with Recurrent Neural Modules
Danijar Hafner, Alex Irpan, James Davidson +1
We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus. Our model consists of recurrent neural modules that send features through a routin…
Robust Adversarial Reinforcement Learning
Lerrel Pinto, James Davidson, Rahul Sukthankar +1
Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based…