2 citations · 2 across the 14 of their papers we have counts for
14 papers
U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanations
Angeliki Dimitriou, Nikolaos Chaidos, Maria Lymperaiou +2
As AI models grow more complex, explainability is essential for building trust, yet concept-based counterfactual methods still face a trade-off between expressivity and efficiency.…
Evaluating Counterfactual Strategic Reasoning in Large Language Models
Dimitrios Georgousis, Maria Lymperaiou, Angeliki Dimitriou +2
We evaluate Large Language Models (LLMs) in repeated game-theoretic settings to assess whether strategic performance reflects genuine reasoning or reliance on memorized patterns. W…
Through the PRISm: Importance-Aware Scene Graphs for Semantic Image Retrieval
Dimitrios Georgoulopoulos, Nikolaos Chaidos, Angeliki Dimitriou +1
Web-based visual search increasingly handles queries driven by semantic intent, focusing on what is happening in an image rather than merely what it looks like. While global embedd…
Sparse Computations in Deep Learning Inference
Ioanna Tasou, Panagiotis Mpakos, Angelos Vlachos +25
The computational demands of modern Deep Neural Networks (DNNs) are immense and constantly growing. While training costs usually capture public attention, inference demands are als…
ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordination
Charidimos Papadakis, Angeliki Dimitriou, Giorgos Filandrianos +3
Large language models show promise for financial decision-making, yet deploying them as autonomous trading agents raises fundamental challenges: how to adapt instructions when rewa…
StockSim: A Dual-Mode Order-Level Simulator for Evaluating Multi-Agent LLMs in Financial Markets
Charidimos Papadakis, Giorgos Filandrianos, Angeliki Dimitriou +3
We present StockSim, an open-source simulation platform for systematic evaluation of large language models (LLMs) in realistic financial decision-making scenarios. Unlike previous…
SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
Nikolaos Chaidos, Angeliki Dimitriou, Maria Lymperaiou +1
Despite the dominance of convolutional and transformer-based architectures in image-to-image retrieval, these models are prone to biases arising from low-level visual features, suc…
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…
HalCECE: A Framework for Explainable Hallucination Detection through Conceptual Counterfactuals in Image Captioning
Maria Lymperaiou, Giorgos Filandrianos, Angeliki Dimitriou +2
In the dynamic landscape of artificial intelligence, the exploration of hallucinations within vision-language (VL) models emerges as a critical frontier. This work delves into the…
Bias Beware: The Impact of Cognitive Biases on LLM-Driven Product Recommendations
Giorgos Filandrianos, Angeliki Dimitriou, Maria Lymperaiou +2
The advent of Large Language Models (LLMs) has revolutionized product recommenders, yet their susceptibility to adversarial manipulation poses critical challenges, particularly in…
Prompt2Fashion: An automatically generated fashion dataset
Georgia Argyrou, Angeliki Dimitriou, Maria Lymperaiou +2
Despite the rapid evolution and increasing efficacy of language and vision generative models, there remains a lack of comprehensive datasets that bridge the gap between personalize…
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…
Structure Your Data: Towards Semantic Graph Counterfactuals
Angeliki Dimitriou, Maria Lymperaiou, Giorgos Filandrianos +2
Counterfactual explanations (CEs) based on concepts are explanations that consider alternative scenarios to understand which high-level semantic features contributed to particular…
Graph Edits for Counterfactual Explanations: A comparative study
Angeliki Dimitriou, Nikolaos Chaidos, Maria Lymperaiou +1
Counterfactuals have been established as a popular explainability technique which leverages a set of minimal edits to alter the prediction of a classifier. When considering concept…