Shenda Hong
Shenda Hong
Other namesShen-Da Hong, 洪申达
Assistant Professor, Peking University
Verified email at - Homepage
Cited by
Cited by
Diffusion models: A comprehensive survey of methods and applications
L Yang, Z Zhang, Y Song, S Hong, R Xu, Y Zhao, W Zhang, B Cui, ...
ACM Computing Surveys 56 (4), 1-39, 2023
Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
S Hong, Y Zhou, J Shang, C Xiao, J Sun
Computers in Biology and Medicine, 103801, 2020
ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks
S Hong, M Wu, Y Zhou, Q Wang, J Shang, H Li, J Xie
2017 Computing in cardiology (cinc), 1-4, 2017
Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
L Yang, S Hong
International conference on machine learning, 25038-25054, 2022
Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings
S Hong, Y Zhou, M Wu, J Shang, Q Wang, H Li, J Xie
Physiological measurement 40 (5), 054009, 2019
MINA: multilevel knowledge-guided attention for modeling electrocardiography signals
S Hong, C Xiao, T Ma, H Li, J Sun
International Joint Conference on Artificial Intelligence (IJCAI) 2019, 2019
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units
S Hong, Y Xu, A Khare, S Priambada, K Maher, A Aljiffry, J Sun, ...
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
A systematic review of echo state networks from design to application
C Sun, M Song, D Cai, B Zhang, S Hong, H Li
IEEE Transactions on Artificial Intelligence 5 (1), 23-37, 2022
Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning
C Sun, S Hong, M Song, H Li, Z Wang
BMC Medical Informatics and Decision Making 21, 1-16, 2021
A review of deep learning methods for irregularly sampled medical time series data
C Sun, S Hong, M Song, H Li
arXiv preprint arXiv:2010.12493, 2020
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning
G Spadon, S Hong, B Brandoli, S Matwin, JF Rodrigues-Jr, J Sun
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Artificial-intelligence-enhanced mobile system for cardiovascular health management
Z Fu, S Hong, R Zhang, S Du
Sensors 21 (3), 773, 2021
Classifying vaguely labeled data based on evidential fusion
M Song, C Sun, D Cai, S Hong, H Li
Information Sciences 583, 159-173, 2022
Intra-inter subject self-supervised learning for multivariate cardiac signals
X Lan, D Ng, S Hong, M Feng
Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 4532-4540, 2022
Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks
WL Zheng, E Amorim, J Jing, W Ge, S Hong, O Wu, M Ghassemi, JW Lee, ...
Resuscitation 169, 86-94, 2021
TEST: Text prototype aligned embedding to activate LLM's ability for time series
C Sun, Y Li, H Li, S Hong
International Conference on Learning Representations 2024, 2024
Diffusion-based scene graph to image generation with masked contrastive pre-training
L Yang, Z Huang, Y Song, S Hong, G Li, W Zhang, B Cui, B Ghanem, ...
arXiv preprint arXiv:2211.11138, 2022
Basiliximab for steroid‐refractory acute graft‐versus‐host disease: a real‐world analysis
XD Mo, SD Hong, YL Zhao, EL Jiang, J Chen, Y Xu, ZM Sun, WJ Zhang, ...
American Journal of Hematology, 2022
Frozen language model helps ECG zero-shot learning
J Li, C Liu, S Cheng, R Arcucci, S Hong
Medical Imaging with Deep Learning, 402-415, 2024
A comprehensive model to predict severe acute graft-versus-host disease in acute leukemia patients after haploidentical hematopoietic stem cell transplantation
MZ Shen, SD Hong, R Lou, RZ Chen, XH Zhang, LP Xu, Y Wang, CH Yan, ...
Experimental hematology & oncology 11 (1), 25, 2022
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