672 citations · 1.6k across the 13 of their papers we have counts for
4 papers · 1 filter
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…
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Fan-Yun Sun, Meng Qu, Jordan Hoffmann +2
This paper focuses on two fundamental tasks of graph analysis: community detection and node representation learning, which capture the global and local structures of graphs, respec…