94 citations · 214 across the 7 of their papers we have counts for
10 papers
TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers
Karel Mundnich, Brandon M. Booth, Michelle L'Hommedieu +11
We present a novel longitudinal multimodal corpus of physiological and behavioral data collected from direct clinical providers in a hospital workplace. We designed the study to in…
An end-to-end approach for the verification problem: learning the right distance
Joao Monteiro, Isabela Albuquerque, Jahangir Alam +2
In this contribution, we augment the metric learning setting by introducing a parametric pseudo-distance, trained jointly with the encoder. Several interpretations are thus drawn f…
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…
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch
João Monteiro, Isabela Albuquerque, Zahid Akhtar +1
Modern applications of artificial neural networks have yielded remarkable performance gains in a wide range of tasks. However, recent studies have discovered that such modelling st…
Speech Dereverberation with Context-aware Recurrent Neural Networks
Joao Felipe Santos, Tiago H. Falk
In this paper, we propose a model to perform speech dereverberation by estimating its spectral magnitude from the reverberant counterpart. Our models are capable of extracting feat…
SRMR variants for improved blind room acoustics characterization
M. Senoussaoui, J. F. Santos, T. H. Falk
Reverberation, especially in large rooms, severely degrades speech recognition performance and speech intelligibility. Since direct measurement of room characteristics is usually n…