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Found 6 papers, 3 papers with code
Reasoning Algorithmically in Graph Neural Networks
Within this context, Neural Algorithmic Reasoning (NAR) stands out as a promising research field, aiming to integrate the structured and rule-based reasoning of algorithms with the adaptive learning capabilities of neural networks, typically by tasking neural models to mimic classical algorithms.
Neural Algorithmic Reasoning for Combinatorial Optimisation
Solving NP-hard/complete combinatorial problems with neural networks is a challenging research area that aims to surpass classical approximate algorithms.
Dual Algorithmic Reasoning
We demonstrate that simultaneously learning the dual definition of these optimisation problems in algorithmic learning allows for better learning and qualitatively better solutions.
Learning heuristics for A*
At training time, we exploit multi-task learning to learn jointly the Dijkstra's algorithm and a consistent heuristic function for the A* search algorithm.
MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks
Explainable AI (XAI) is a research area whose objective is to increase trustworthiness and to enlighten the hidden mechanism of opaque machine learning techniques.
Explaining Deep Graph Networks with Molecular Counterfactuals
We present a novel approach to tackle explainability of deep graph networks in the context of molecule property prediction tasks, named MEG (Molecular Explanation Generator).
