244 citations · 824 across the 56 of their papers we have counts for
12 papers · 1 filter
Metanetworks as Regulatory Operators: Learning to Edit for Requirement Compliance
Ioannis Kalogeropoulos, Giorgos Bouritsas, Yannis Panagakis
As machine learning models are increasingly deployed in high-stakes settings, e.g. as decision support systems in various societal sectors or in critical infrastructure, designers…
EEG-D3: A Solution to the Hidden Overfitting Problem of Deep Learning Models
Siegfried Ludwig, Stylianos Bakas, Konstantinos Barmpas +5
Deep learning for decoding EEG signals has gained traction, with many claims to state-of-the-art accuracy. However, despite the convincing benchmark performance, successful transla…
Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search
Manos Plitsis, Giorgos Bouritsas, Vassilis Katsouros +1
Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gende…
Alpha Divergence Losses for Biometric Verification
Dimitrios Koutsianos, Ladislav Mosner, Yannis Panagakis +1
Performance in face and speaker verification is largely driven by margin-based softmax losses such as CosFace and ArcFace. Recently introduced -divergence loss functions offer a…
Language Models are Injective and Hence Invertible
Giorgos Nikolaou, Tommaso Mencattini, Donato Crisostomi +3
Transformer components such as non-linear activations and normalization are inherently non-injective, suggesting that different inputs could map to the same output and prevent exac…
NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models
Konstantinos Barmpas, Na Lee, Dimitrios Chalatsis +7
Biosignals such as electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) encode physiological activity across multiple temporal and spectral scales, y…
Environment-Aware Satellite Image Generation with Diffusion Models
Nikos Kostagiolas, Pantelis Georgiades, Yannis Panagakis +1
Diffusion-based foundation models have recently garnered much attention in the field of generative modeling due to their ability to generate images of high quality and fidelity. Al…
A Principled Framework for Multi-View Contrastive Learning
Panagiotis Koromilas, Efthymios Georgiou, Giorgos Bouritsas +3
Contrastive Learning (CL), a leading paradigm in Self-Supervised Learning (SSL), typically relies on pairs of data views generated through augmentation. While multiple augmentation…
Are Large Brainwave Foundation Models Capable Yet? Insights from Fine-tuning
Na Lee, Konstantinos Barmpas, Yannis Panagakis +3
Foundation Models have demonstrated significant success across various domains in Artificial Intelligence (AI), yet their capabilities for brainwave modeling remain unclear. In thi…
Synthetic Speech Source Tracing using Metric Learning
Dimitrios Koutsianos, Stavros Zacharopoulos, Yannis Panagakis +1
This paper addresses source tracing in synthetic speech-identifying generative systems behind manipulated audio via speaker recognition-inspired pipelines. While prior work focuses…
Causally Steered Diffusion for Automated Video Counterfactual Generation
Nikos Spyrou, Athanasios Vlontzos, Paraskevas Pegios +5
Adapting text-to-image (T2I) latent diffusion models (LDMs) to video editing has shown strong visual fidelity and controllability, but challenges remain in maintaining causal relat…
Advancing Brainwave Modeling with a Codebook-Based Foundation Model
Konstantinos Barmpas, Na Lee, Yannis Panagakis +3
Recent advances in large-scale pre-trained Electroencephalogram (EEG) models have shown great promise, driving progress in Brain-Computer Interfaces (BCIs) and healthcare applicati…