1 citations · 1 across the 7 of their papers we have counts for
7 papers
Enhancing Small Language Models Reasoning through Knowledge Graph Grounding
Dimitrios Kelesis, Konstantinos Bougiatiotis, Georgios Paliouras
Although large language models (LLMs) have set benchmarks for zero-shot reasoning, their deployment remains cost-prohibitive and environmentally taxing. Small Language Models (SLMs…
Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools
Konstantinos Bougiatiotis, Dimitrios Kelesis, Georgios Paliouras
Small language models (SLMs) have shown promise for zero-shot molecular property prediction from SMILES strings, yet they often suffer from structural blindness because sequence re…
Analyzing the Effect of Embedding Norms and Singular Values to Oversmoothing in Graph Neural Networks
Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras
In this paper, we study the factors that contribute to the effect of oversmoothing in deep Graph Neural Networks (GNNs). Specifically, our analysis is based on a new metric (Mean A…
Reducing Oversmoothing through Informed Weight Initialization in Graph Neural Networks
Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras
In this work, we generalize the ideas of Kaiming initialization to Graph Neural Networks (GNNs) and propose a new scheme (G-Init) that reduces oversmoothing, leading to very good r…
Partially Trained Graph Convolutional Networks Resist Oversmoothing
Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras
In this work we investigate an observation made by Kipf \& Welling, who suggested that untrained GCNs can generate meaningful node embeddings. In particular, we investigate the eff…
Detecting and analysing the topology of the cosmic web with spatial clustering algorithms I: Methods
Dimitrios Kelesis, Spyros Basilakos, Vicky Papadopoulou Lesta +2
In this paper we explore the use of spatial clustering algorithms as a new computational approach for modeling the cosmic web. We demonstrate that such algorithms are efficient in…
Tree-based Focused Web Crawling with Reinforcement Learning
Andreas Kontogiannis, Dimitrios Kelesis, Vasilis Pollatos +2
A focused crawler aims at discovering as many web pages and web sites relevant to a target topic as possible, while avoiding irrelevant ones. Reinforcement Learning (RL) has been a…