5 papers
UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
Dobrik Georgiev, Kheeran Naidu, Alberto Cattaneo +3
Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon often known as hallucination). Retrieval…
KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems
Stjepan Požgaj, Dobrik Georgiev, Marin Å iliÄ +1
Neural algorithmic reasoning (NAR) is a growing field that aims to embed algorithmic logic into neural networks by imitating classical algorithms. In this extended abstract, we det…
Neural Algorithmic Reasoning with Multiple Correct Solutions
Zeno Kujawa, John Poole, Dobrik Georgiev +3
Neural Algorithmic Reasoning (NAR) extends classical algorithms to higher dimensional data. However, canonical implementations of NAR train neural networks to return only a single…
Deep Equilibrium Algorithmic Reasoning
Dobrik Georgiev, JJ Wilson, Davide Buffelli +1
Neural Algorithmic Reasoning (NAR) research has demonstrated that graph neural networks (GNNs) could learn to execute classical algorithms. However, most previous approaches have a…
Neural Bipartite Matching
Dobrik Georgiev, Pietro Liò
Graph neural networks (GNNs) have found application for learning in the space of algorithms. However, the algorithms chosen by existing research (sorting, Breadth-First search, sho…