755 citations · 4.3k across the 133 of their papers we have counts for
19 papers · 1 filter
EMG-GPT: Predictive Pretraining on Residual-Quantized EMG Tokens for Hand Pose Estimation
Ettore Magni, Rolandos Alexandros Potamias, Stefanos Zafeiriou +1
Surface electromyography (sEMG) is a low-power, cost-effective biosignal for hand-pose estimation and gesture classification. In this work, we examine whether self-supervised pretr…
Air Quality Downscaling with Station-Guided Pseudo-Supervision
Guorun Wang, Simone Foti, Andreas D. Demou +5
Super-resolving coarse atmospheric fields to local PM variations is uniquely challenged by a mismatch in spatial support: while pixels represent regional averages, ground-t…
Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models
Urban Širca, Maryam Alimardani, Stefanos Zafeiriou +1
EEG foundation models (EEG-FMs) have been evaluated predominantly on clean, in-distribution accuracy, demonstrating modest gains over supervised baselines and weak frozen represent…
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…
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…
Large Learning Rates Simultaneously Achieve Robustness to Spurious Correlations and Compressibility
Melih Barsbey, Lucas Prieto, Stefanos Zafeiriou +1
Robustness and resource-efficiency are two highly desirable properties for modern machine learning models. However, achieving them jointly remains a challenge. In this paper, we id…
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…
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…
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters
Siegfried Ludwig, Stylianos Bakas, Dimitrios A. Adamos +3
Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions. However, the relevant f…
Binary Graph Neural Networks
Mehdi Bahri, Gaétan Bahl, Stefanos Zafeiriou
Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. As they generalize the operations of classical CNNs on…
Deep Polynomial Neural Networks
Grigorios Chrysos, Stylianos Moschoglou, Giorgos Bouritsas +3
Deep Convolutional Neural Networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. T…
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou +1
While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the stru…
nets: Deep Polynomial Neural Networks
Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas +3
Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. Th…
Analysing Affective Behavior in the First ABAW 2020 Competition
Dimitrios Kollias, Attila Schulc, Elnar Hajiyev +1
The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the first Competition aiming at automatic analysis of the three main behavior tasks of valence-arousal estima…
Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Dataset
Dimitrios Kollias, Stefanos Zafeiriou
This paper presents a novel CNN-RNN based approach, which exploits multiple CNN features for dimensional emotion recognition in-the-wild, utilizing the One-Minute Gradual-Emotion (…
PolyGAN: High-Order Polynomial Generators
Grigorios Chrysos, Stylianos Moschoglou, Yannis Panagakis +1
Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions. Since their advent, numerous va…
Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm
Dimitrios Kollias, Stefanos Zafeiriou
A novel procedure is presented in this paper, for training a deep convolutional and recurrent neural network, taking into account both the available training data set and some info…
Robust Conditional Generative Adversarial Networks
Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou
Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision. The major…
A Unified Framework for Probabilistic Component Analysis
Mihalis A. Nicolaou, Stefanos Zafeiriou, Maja Pantic
We present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood, thus providing an ele…