3 citations · 3 across the 8 of their papers we have counts for
8 papers
B[FM]: Brain Foundation Model via Flow Matching with SplitUNet
Jaedong Hwang, Kathleen Zhang, Wei Dai +5
EEG foundation models can learn generalizable representations from large-scale EEG corpora to enable single-backbone transfer across diverse clinical and brain-computer interface t…
Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals
Phu X. Nguyen, Konstantinos Kontras, Wei Dai +5
Electrocardiograms (ECGs) are widely used non-invasive measurements of cardiac activity and play a central role in clinical diagnosis. Recent multimodal approaches align ECG signal…
NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
Konstantinos Kontras, Trui Osselaer, Stylianos G. Mouslech +12
Foundation models (FMs) promise to extract unified representations that generalize across downstream tasks. They have emerged across fields, including electroencephalography (EEG),…
SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning
Konstantinos Kontras, Teodora Gagaleska, Thomas Strypsteen +4
A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities, and is not available from an…
The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment
Stefanos Koutoupis, Michaela Areti Zervou, Konstantinos Kontras +3
Learning joint representations across multiple modalities remains a central challenge in multimodal machine learning. Prevailing approaches predominantly operate in pairwise settin…
Balancing Multimodal Training Through Game-Theoretic Regularization
Konstantinos Kontras, Thomas Strypsteen, Christos Chatzichristos +3
Multimodal learning holds promise for richer information extraction by capturing dependencies across data sources. Yet, current training methods often underperform due to modality…
Improving Multimodal Learning with Multi-Loss Gradient Modulation
Konstantinos Kontras, Christos Chatzichristos, Matthew Blaschko +1
Learning from multiple modalities, such as audio and video, offers opportunities for leveraging complementary information, enhancing robustness, and improving contextual understand…
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
Konstantinos Kontras, Christos Chatzichristos, Huy Phan +2
Sleep abnormalities can have severe health consequences. Automated sleep staging, i.e. labelling the sequence of sleep stages from the patient's physiological recordings, could sim…