87 citations · 157 across the 7 of their papers we have counts for
6 papers · 1 filter
Self-supervised representation learning from electroencephalography signals
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen +3
The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains. Self-supervised learning, a paradi…
Generalizing to unseen domains via distribution matching
Isabela Albuquerque, João Monteiro, Mohammad Darvishi +2
Supervised learning results typically rely on assumptions of i.i.d. data. Unfortunately, those assumptions are commonly violated in practice. In this work, we tackle such problem b…
Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment
Isabela Albuquerque, João Monteiro, Olivier Rosanne +3
Assessment of mental workload in real-world conditions is key to ensure the performance of workers executing tasks that demand sustained attention. Previous literature has employed…
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Isabela Albuquerque, João Monteiro, Thang Doan +3
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involvin…
Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation
Isabela Albuquerque, João Monteiro, Tiago H. Falk
In this work, we introduce a two-step framework for generative modeling of temporal data. Specifically, the generative adversarial networks (GANs) setting is employed to generate s…
Deep learning-based electroencephalography analysis: a systematic review
Yannick Roy, Hubert Banville, Isabela Albuquerque +3
Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in help…