Comprehensive evaluations on EEG, electrocardiogram (ECG), and human activity sensory signals demonstrate that \method outperforms robust baselines in common settings and facilitates learning across multiple datasets with different formats.
Extensive experiments show that ManyDG can boost the generalization performance on multiple real-world healthcare tasks (e. g., 3. 7% Jaccard improvements on MIMIC drug recommendation) and support realistic but challenging settings such as insufficient data and continuous learning.
We respond to the national Pediatric COVID-19 data challenge with a novel machine learning model, MedML.
Objective: In this paper, we aim to learn robust vector representations from massive unlabeled EEG signals, such that the learned vectorized features (1) are expressive enough to replace the raw signals in the sleep staging task; and (2) provide better predictive performance than supervised models in scenarios of fewer labels and noisy samples.
This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification.
Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares.
On a benchmark dataset, our SafeDrug is relatively shown to reduce DDI by 19. 43% and improves 2. 88% on Jaccard similarity between recommended and actually prescribed drug combinations over previous approaches.
Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions.
Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss.
We further compare policies that rely on partial venue closure to policies that espouse wide-spread periodic testing instead (i. e., in lieu of social distancing).
Physics and Society Computers and Society Social and Information Networks
Oversmoothing has been assumed to be the major cause of performance drop in deep graph convolutional networks (GCNs).
Online Real-Time Bidding (RTB) is a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs.
In the synthetic dataset, our model reduces error by 40%.
We develop a novel induced relational graph convolutional network (IR-GCN) framework to address the question.