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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…
Graph-Coupled Oscillator Networks
T. Konstantin Rusch, Benjamin P. Chamberlain, James Rowbottom +2
We propose Graph-Coupled Oscillator Networks (GraphCON), a novel framework for deep learning on graphs. It is based on discretizations of a second-order system of ordinary differen…
Graph Kernel Neural Networks
Luca Cosmo, Giorgia Minello, Alessandro Bicciato +4
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is rea…