6 citations · 10 across the 15 of their papers we have counts for
15 papers
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration
Andreas Kontogiannis, Konstantinos Papathanasiou, Yi Shen +3
Learning to cooperate in distributed partially observable environments with no communication abilities poses significant challenges for multi-agent deep reinforcement learning (MAR…
Explaining Vision GNNs: A Semantic and Visual Analysis of Graph-based Image Classification
Nikolaos Chaidos, Angeliki Dimitriou, Nikolaos Spanos +2
Graph Neural Networks (GNNs) have emerged as an efficient alternative to convolutional approaches for vision tasks such as image classification, leveraging patch-based representati…
ARPA: A Novel Hybrid Model for Advancing Visual Word Disambiguation Using Large Language Models and Transformers
Aristi Papastavrou, Maria Lymperaiou, Giorgos Stamou
In the rapidly evolving fields of natural language processing and computer vision, Visual Word Sense Disambiguation (VWSD) stands as a critical, yet challenging task. The quest for…
Automatic Generation of Fashion Images using Prompting in Generative Machine Learning Models
Georgia Argyrou, Angeliki Dimitriou, Maria Lymperaiou +2
The advent of artificial intelligence has contributed in a groundbreaking transformation of the fashion industry, redefining creativity and innovation in unprecedented ways. This w…
Beyond One-Size-Fits-All: Adapting Counterfactual Explanations to User Objectives
Orfeas Menis Mastromichalakis, Jason Liartis, Giorgos Stamou
Explainable Artificial Intelligence (XAI) has emerged as a critical area of research aimed at enhancing the transparency and interpretability of AI systems. Counterfactual Explanat…
AILS-NTUA at SemEval-2024 Task 6: Efficient model tuning for hallucination detection and analysis
Natalia Grigoriadou, Maria Lymperaiou, Giorgos Filandrianos +1
In this paper, we present our team's submissions for SemEval-2024 Task-6 - SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakes. The participants…