91 citations · 236 across the 31 of their papers we have counts for
6 papers · 1 filter
Born Identity Network: Multi-way Counterfactual Map Generation to Explain a Classifier's Decision
Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
There exists an apparent negative correlation between performance and interpretability of deep learning models. In an effort to reduce this negative correlation, we propose a Born…
A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs
Wonjun Ko, Eunjin Jeon, Heung-Il Suk
In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial in the form of a Markov decision process and propo…
Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease Progression
Wonsik Jung, Eunji Jun, Heung-Il Suk
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In…
Multi-Scale Neural network for EEG Representation Learning in BCI
Wonjun Ko, Eunjin Jeon, Seungwoo Jeong +1
Recent advances in deep learning have had a methodological and practical impact on brain-computer interface research. Among the various deep network architectures, convolutional ne…
Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction
Eunji Jun, Ahmad Wisnu Mulyadi, Jaehun Choi +1
Electronic health records (EHR) are characterized as non-stationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inher…
Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series
Ahmad Wisnu Mulyadi, Eunji Jun, Heung-Il Suk
Electronic health records (EHR) consist of longitudinal clinical observations portrayed with sparsity, irregularity, and high-dimensionality, which become major obstacles in drawin…