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
2522017
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
1332015
Neural document embeddings for intensive care patient mortality prediction
P Grnarova, F Schmidt, SL Hyland, C Eickhoff
arXiv preprint arXiv:1612.00467, 2016
422016
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
302020
Learning Unitary Operators with Help From u (n)
SL Hyland, G Rätsch
AAAI 2017, 2016
282016
Real-valued (medical) time series generation with recurrent conditional gans
S Hyland, C Esteban, G Rätsch
182018
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
122018
Real-valued (medical) time series generation with recurrent conditional GANs. arXiv 2017
C Esteban, SL Hyland, G Rätsch
arXiv preprint arXiv:1706.02633, 0
8
A generative model of words and relationships from multiple sources
SL Hyland, T Karaletsos, G Rätsch
Association for the Advancement of Artificial Intelligence, 2016
72016
Real-valued (medical) time series generation with recurrent conditional gans, 2017
C Esteban, SL Hyland, G Rätsch
URL http://arxiv. org/abs, 1811
71811
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
62019
On the intrinsic privacy of stochastic gradient descent
SL Hyland, S Tople
arXiv preprint arXiv:1912.02919, 2019
42019
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
22020
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
22019
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
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
12021
Predicting Length of Stay in the Intensive Care Unit with Temporal Pointwise Convolutional Networks
E Rocheteau, P Liò, S Hyland
arXiv preprint arXiv:2006.16109, 2020
12020
Predicting circulatory system deterioration in intensive care unit patients
SL Hyland, M Hüser, X Lyu, M Faltys, T Merz, G Rätsch
AIH@ IJCAI, 2018
12018
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