3.7k citations · 7.1k across the 79 of their papers we have counts for
88 papers
On the Limitations of Fractal Dimension as a Measure of Generalization
Charlie B. Tan, Inés García-Redondo, Qiquan Wang +2
Bounding and predicting the generalization gap of overparameterized neural networks remains a central open problem in theoretical machine learning. There is a recent and growing bo…
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein +2
Discrepancy is a well-known measure for the irregularity of the distribution of a point set. Point sets with small discrepancy are called low-discrepancy and are known to efficient…
Revealing Decurve Flows for Generalized Graph Propagation
Chen Lin, Liheng Ma, Yiyang Chen +3
This study addresses the limitations of the traditional analysis of message-passing, central to graph learning, by defining {\em \textbf{generalized propagation}} with directed and…
Setting the Record Straight on Transformer Oversmoothing
Gbètondji J-S Dovonon, Michael M. Bronstein, Matt J. Kusner
Transformer-based models have recently become wildly successful across a diverse set of domains. At the same time, recent work has shown empirically and theoretically that Transfor…
Heterogeneous manifolds for curvature-aware graph embedding
Francesco Di Giovanni, Giulia Luise, Michael Bronstein
Graph embeddings, wherein the nodes of the graph are represented by points in a continuous space, are used in a broad range of Graph ML applications. The quality of such embeddings…
RECOVER: sequential model optimization platform for combination drug repurposing identifies novel synergistic compounds in vitro
Paul Bertin, Jarrid Rector-Brooks, Deepak Sharma +17
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dos…