State-of-the-art neural algorithmic reasoners make use of message passing in graph neural
networks (GNNs). But typical GNNs blur the distinction between the definition and invocation …

Counterfactual thinking is a critical yet challenging topic for artificial intelligence to learn
knowledge from data and ultimately improve their performances for new scenarios. Many …

Neural algorithmic reasoners are parallel processors. Teaching them sequential algorithms
contradicts this nature, rendering a significant share of their computations redundant …

Neural algorithmic reasoning is an emerging area of machine learning focusing on building
models that can imitate the execution of classic algorithms, such as sorting, shortest paths …

Neural algorithmic reasoning is an emerging research direction that endows neural
networks with the ability to mimic algorithmic executions step-by-step. A common paradigm …

The execution of graph algorithms using neural networks has recently attracted significant
interest due to promising empirical progress. This motivates further understanding of how …

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 …

ChatGPT is a language model created by OpenAI, utilising neural networks and the
transformer architecture for Natural Language Processing (NLP) tasks. The model's …

Recently, researchers have proposed ML-driven traffic engineering (TE) schemes where a
neural network model is used to produce TE decisions in lieu of conventional optimization …

We introduce an extension to the CLRS algorithmic learning benchmark, prioritizing
scalability and the utilization of sparse representations. Many algorithms in CLRS require …