632 citations · 2.1k across the 24 of their papers we have counts for
14 papers · 1 filter
CircuitGate: Logic-Consistent Circuit-Level Functional Modeling for And-Inverter Graphs
Qifan Zhang, Ruijie Li, Fangzhou Zhang +6
And-Inverter Graphs (AIGs) are fundamental representations for logic synthesis and verification in Electronic Design Automation (EDA). As structured representations of complex digi…
Generative Model via Quantile Assignment
Georgi Hrusanov, Oliver Y. Chén, Julien S. Bodelet
Deep Generative models (DGMs) play two key roles in modern machine learning: (i) producing new information (e.g., image synthesis) and (ii) reducing dimensionality. However, tradit…
Physics-Informed Neural Koopman Machine for Interpretable Longitudinal Personalized Alzheimer's Disease Forecasting
Georgi Hrusanov, Duy-Thanh Vu, Duy-Cat Can +6
Early forecasting of individual cognitive decline in Alzheimer's disease (AD) is central to disease evaluation and management. Despite advances, it is as of yet challenging for exi…
MUDI: A Multimodal Biomedical Dataset for Understanding Pharmacodynamic Drug-Drug Interactions
Tung-Lam Ngo, Ba-Hoang Tran, Duy-Cat Can +3
Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing DDI datas…
Explainable Graph-theoretical Machine Learning with Application to Alzheimer's Disease Prediction
Narmina Baghirova, Duy-Thanh Vũ, Duy-Cat Can +6
Dementia affects over 55 million people worldwide, projected to reach 139 million by 2050, with Alzheimer's disease (AD) accounting for 60-70% of cases. AD is associated with disru…
Enhancing Uncertain Demand Prediction in Hospitals Using Simple and Advanced Machine Learning
Annie Hu, Samuel Stockman, Xun Wu +3
Early and timely prediction of patient care demand not only affects effective resource allocation but also influences clinical decision-making as well as patient experience. Accura…
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
Huy Phan, Kaare Mikkelsen, Oliver Y. Chén +3
Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards inter…
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
Huy Phan, Kaare Mikkelsen, Oliver Y. Chén +4
Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteri…
Improving GANs for Speech Enhancement
Huy Phan, Ian V. McLoughlin, Lam Pham +4
Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use…
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
Huy Phan, Oliver Y. Chén, Philipp Koch +4
Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a…
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch
Huy Phan, Oliver Y. Chén, Philipp Koch +2
Many sleep studies suffer from the problem of insufficient data to fully utilize deep neural networks as different labs use different recordings set ups, leading to the need of tra…
Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
Huy Phan, Oliver Y. Chén, Philipp Koch +4
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events. The framework leverages…
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
Huy Phan, Fernando Andreotti, Navin Cooray +2
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time…
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
Huy Phan, Fernando Andreotti, Navin Cooray +2
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This work proposes a joint classification-and-prediction framework based on CNNs for aut…