15 citations · 40 across the 21 of their papers we have counts for
8 papers · 1 filter
Amplifying human performance in combinatorial competitive programming
Petar Veličković, Alex Vitvitskyi, Larisa Markeeva +4
Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady prog…
NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms
Efimia Panagiotaki, Daniele De Martini, Lars Kunze +2
This study explores the intersection of neural networks and classical robotics algorithms through the Neural Algorithmic Reasoning (NAR) blueprint, enabling the training of neural…
Cayley Graph Propagation
JJ Wilson, Maya Bechler-Speicher, Petar Veličković
In spite of the plethora of success stories with graph neural networks (GNNs) on modelling graph-structured data, they are notoriously vulnerable to over-squashing, whereby tasks n…
Recurrent Aggregators in Neural Algorithmic Reasoning
Kaijia Xu, Petar Veličković
Neural algorithmic reasoning (NAR) is an emerging field that seeks to design neural networks that mimic classical algorithmic computations. Today, graph neural networks (GNNs) are…
Commute-Time-Optimised Graphs for GNNs
Igor Sterner, Shiye Su, Petar Veličković
We explore graph rewiring methods that optimise commute time. Recent graph rewiring approaches facilitate long-range interactions in sparse graphs, making such rewirings commute-ti…
Temporal Graph Rewiring with Expander Graphs
Katarina Petrović, Shenyang Huang, Farimah Poursafaei +1
Evolving relations in real-world networks are often modelled by temporal graphs. Temporal Graph Neural Networks (TGNNs) emerged to model evolutionary behaviour of such graphs by le…
Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
Bruno Gavranović, Paul Lessard, Andrew Dudzik +3
We present our position on the elusive quest for a general-purpose framework for specifying and studying deep learning architectures. Our opinion is that the key attempts made so f…
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new fron…