Stephanie L Hyland
Stephanie L Hyland
Microsoft Research Cambridge
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
Real-valued (medical) time series generation with recurrent conditional gans
C Esteban, SL Hyland, G Rätsch
arXiv preprint arXiv:1706.02633, 2017
3182017
Identification of active transcriptional regulatory elements from GRO-seq data
CG Danko, SL Hyland, LJ Core, AL Martins, CT Waters, HW Lee, ...
Nature methods 12 (5), 433-438, 2015
1442015
Early prediction of circulatory failure in the intensive care unit using machine learning
SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch, C Esteban, C Bock, ...
Nature medicine 26 (3), 364-373, 2020
592020
Neural document embeddings for intensive care patient mortality prediction
P Grnarova, F Schmidt, SL Hyland, C Eickhoff
arXiv preprint arXiv:1612.00467, 2016
442016
Learning Unitary Operators with Help From u (n)
SL Hyland, G Rätsch
AAAI 2017, 2016
302016
Improving clinical predictions through unsupervised time series representation learning
X Lyu, M Hueser, SL Hyland, G Zerveas, G Raetsch
arXiv preprint arXiv:1812.00490, 2018
222018
Real-valued (medical) time series generation with recurrent conditional gans
S Hyland, C Esteban, G Rätsch
212018
A global metagenomic map of urban microbiomes and antimicrobial resistance
D Danko, D Bezdan, EE Afshin, S Ahsanuddin, C Bhattacharya, DJ Butler, ...
Cell, 2021
122021
Real-valued (medical) time series generation with recurrent conditional gans (2017)
C Esteban, SL Hyland, G Rätsch
arXiv preprint arXiv:1706.02633, 2019
122019
Real-valued (medical) time series generation with recurrent conditional GANs. arXiv 2017
C Esteban, SL Hyland, G Rätsch
arXiv preprint arXiv:1706.02633, 0
11
A generative model of words and relationships from multiple sources
SL Hyland, T Karaletsos, G Rätsch
Association for the Advancement of Artificial Intelligence, 2016
92016
Machine learning for early prediction of circulatory failure in the intensive care unit
SL Hyland, M Faltys, M Hüser, X Lyu, T Gumbsch, C Esteban, C Bock, ...
arXiv preprint arXiv:1904.07990, 2019
72019
On the intrinsic privacy of stochastic gradient descent
SL Hyland, S Tople
arXiv preprint arXiv:1912.02919, 2019
72019
Real-Valued (Medical) Time Series Generation with Recurrent Conditional GANs.” arXiv e-prints
C Esteban, SL Hyland, G Rätsch
arXiv preprint arXiv:1706.02633, 2017
52017
Machine Learning for Health (ML4H) 2020: Advancing Healthcare for All
SK Sarkar, S Roy, E Alsentzer, MBA McDermott, F Falck, I Bica, G Adams, ...
Machine Learning for Health, 1-11, 2020
42020
Temporal pointwise convolutional networks for length of stay prediction in the intensive care unit
E Rocheteau, P Liò, S Hyland
Proceedings of the Conference on Health, Inference, and Learning, 58-68, 2021
32021
Unsupervised extraction of phenotypes from cancer clinical notes for association studies
SG Stark, SL Hyland, MF Pradier, K Lehmann, A Wicki, FP Cruz, JE Vogt, ...
arXiv preprint arXiv:1904.12973, 2019
32019
Knowledge transfer with medical language embeddings
SL Hyland, T Karaletsos, G Rätsch
arXiv preprint arXiv:1602.03551, 2016
22016
Largescale sentence clustering from electronic health records for genetic associations in cancer
MF Pradier, S Stark, S Hyland, JE Vogt, G Rätsch
Machine Learning for Computational Biology Workshop in Neural Information …, 2015
22015
Accurate identification of active transcriptional regulatory elements from global run-on and sequencing data
CG Danko, SL Hyland, LJ Core, AL Martins, CT Waters, HW Lee, ...
bioRxiv, 011353, 2014
22014
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