69 citations · 146 across the 12 of their papers we have counts for
14 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…
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…
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Velickovic +1
Learning to execute algorithms is a fundamental problem that has been widely studied. Prior work~\cite{veli19neural} has shown that to enable systematic generalisation on graph alg…
Relating Graph Neural Networks to Structural Causal Models
Matej Zečević, Devendra Singh Dhami, Petar Veličković +1
Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations. For most processes of…