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08/16/2023
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
This work summarizes two strategies for completing time-series (TS) task...
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08/04/2023
VQGraph: Graph Vector-Quantization for Bridging GNNs and MLPs
Graph Neural Networks (GNNs) conduct message passing which aggregates lo...
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06/28/2023
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization
Out-of-distribution (OOD) graph generalization are critical for many rea...
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03/22/2023
Frozen Language Model Helps ECG Zero-Shot Learning
The electrocardiogram (ECG) is one of the most commonly used non-invasiv...
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02/07/2023
Towards Better Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach
Not all positive pairs are beneficial to time series contrastive learnin...
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10/06/2022
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks
Continuous diagnosis and prognosis are essential for intensive care pati...
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09/02/2022
Diffusion Models: A Comprehensive Survey of Methods and Applications
Diffusion models are a class of deep generative models that have shown i...
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08/14/2022
Confidence-Guided Learning Process for Continuous Classification of Time Series
In the real world, the class of a time series is usually labeled at the ...
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05/31/2022
Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning
Unsupervised/self-supervised graph representation learning is critical f...
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05/20/2022
Cross Reconstruction Transformer for Self-Supervised Time Series Representation Learning
Unsupervised/self-supervised representation learning in time series is c...
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05/19/2022
Spatial Autoregressive Coding for Graph Neural Recommendation
Graph embedding methods including traditional shallow models and deep Gr...
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03/14/2022
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training
In clinics, doctors rely on electrocardiograms (ECGs) to assess severe c...
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02/26/2022
A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings
With the development of deep learning-based methods, automated classific...
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02/25/2022
Learning ECG Representations based on Manipulated Temporal-Spatial Reverse Detection
Learning representations from electrocardiogram (ECG) serves as a fundam...
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02/25/2022
MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs
Ventricular arrhythmias (VA) are the main causes of sudden cardiac death...
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02/08/2022
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
Unsupervised/self-supervised time series representation learning is a ch...
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02/08/2022
Spectral Propagation Graph Network for Few-shot Time Series Classification
Few-shot Time Series Classification (few-shot TSC) is a challenging prob...
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09/18/2021
Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals
Learning information-rich and generalizable representations effectively ...
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08/31/2021
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization
Since data is presented long-tailed in reality, it is challenging for Fe...
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05/02/2021
TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data
Prediction based on Irregularly Sampled Time Series (ISTS) is of wide co...
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12/05/2020
A Review of Designs and Applications of Echo State Networks
Recurrent Neural Networks (RNNs) have demonstrated their outstanding abi...
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10/23/2020
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
Irregularly sampled time series (ISTS) data has irregular temporal inter...
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08/28/2020
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning
Time-series forecasting is one of the most active research topics in pre...
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08/10/2020
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units
Deep learning models have achieved expert-level performance in healthcar...
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07/04/2020
CardioLearn: A Cloud Deep Learning Service for Cardiac Disease Detection from Electrocardiogram
Electrocardiogram (ECG) is one of the most convenient and non-invasive t...
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05/21/2020
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
There is a growing interest in applying deep learning (DL) to healthcare...
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12/28/2019
Opportunities and Challenges in Deep Learning Methods on Electrocardiogram Data: A Systematic Review
Objective: To conduct a systematic review of deep learning methods on El...
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05/27/2019
MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals
Electrocardiography (ECG) signals are commonly used to diagnose various ...
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09/06/2018