309 citations · 476 across the 24 of their papers we have counts for
7 papers · 1 filter
Half-Hop: A graph upsampling approach for slowing down message passing
Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor +6
Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail whe…
Geometric Epitope and Paratope Prediction
Marco Pegoraro, Clémentine Dominé, Emanuele Rodolà +2
Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules. In this paper, we investigate the optimal representation for predicting…
Parallel Algorithms Align with Neural Execution
Valerie Engelmayer, Dobrik Georgiev, Petar Veličković
Neural algorithmic reasoners are parallel processors. Teaching them sequential algorithms contradicts this nature, rendering a significant share of their computations redundant. Pa…
Recursive Algorithmic Reasoning
Jonas Jürß, Dulhan Jayalath, Petar Veličković
Learning models that execute algorithms can enable us to address a key problem in deep learning: generalizing to out-of-distribution data. However, neural networks are currently un…
Dual Algorithmic Reasoning
Danilo Numeroso, Davide Bacciu, Petar Veličković
Neural Algorithmic Reasoning is an emerging area of machine learning which seeks to infuse algorithmic computation in neural networks, typically by training neural models to approx…
Everything is Connected: Graph Neural Networks
Petar Veličković
In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are ele…