161 citations · 318 across the 3 of their papers we have counts for
4 papers
Unifying Grokking and Double Descent
Xander Davies, Lauro Langosco, David Krueger
A principled understanding of generalization in deep learning may require unifying disparate observations under a single conceptual framework. Previous work has studied \emph{grokk…
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal neurons
Irina Higgins, Le Chang, Victoria Langston +4
Deep supervised neural networks trained to classify objects have emerged as popular models of computation in the primate ventral stream. These models represent information with a h…
Relational Deep Reinforcement Learning
Vinicius Zambaldi, David Raposo, Adam Santoro +13
We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through st…
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst +24
Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, i…