243 citations · 266 across the 4 of their papers we have counts for
4 papers · 1 filter
AlgebraNets
Jordan Hoffmann, Simon Schmitt, Simon Osindero +2
Neural networks have historically been built layerwise from the set of functions in , i.e. with activations and weights/parameters represented…
Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada +3
Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative models have led to rapid progress in aut…
Recurrent Independent Mechanisms
Anirudh Goyal, Alex Lamb, Jordan Hoffmann +4
Learning modular structures which reflect the dynamics of the environment can lead to better generalization and robustness to changes which only affect a few of the underlying caus…
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma +1
This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-wo…