Davide Buffelli

I'll be at NeurIPS next week presenting two papers (one on the generalization properties of 2nd order methods, and on a new model for Neural Algorithmic Reasoning). Feel free to reach out if you'd like to have a chat about optimization, graphs, foundation models for time series, or AI 4 chip design

I'm also going to present the work in-person at the Oxford meetup (log-ox.github.io). Feel free to contact me if you'd like to have a chat!

CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs Graph neural networks have become the default choice by practitioners for graph learning tasks such as graph classification and node classification. Nevertheless, popular graph neural network models s...

If you're curious, you can check out the preprint to read how we take advantage of persistent homology to provide higher-order information to GNNs arxiv.org/abs/2409.08217

Very happy that our paper "CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs" has been accepted at LoG! a huge thanks to my co-authors Farzin Soleymani and @pseudomanifold.topology.rocks

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