Search Results for author:
Found 5 papers, 2 papers with code
Finding path and cycle counting formulae in graphs with Deep Reinforcement Learning
This paper presents Grammar Reinforcement Learning (GRL), a reinforcement learning algorithm that uses Monte Carlo Tree Search (MCTS) and a transformer architecture that models a Pushdown Automaton (PDA) within a context-free grammar (CFG) framework.
Technical report: Graph Neural Networks go Grammatical
This paper introduces a framework for formally establishing a connection between a portion of an algebraic language and a Graph Neural Network (GNN).
Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?
Graph matching is an important problem that has received widespread attention, especially in the field of computer vision.
A Convolutional Neural Network into graph space
Convolution and pooling operators are defined in graph domain.
Graph edit distance : a new binary linear programming formulation
Moreover, a relaxation of the domain constraints in the formulations provides efficient lower bound approximations of the GED.
