Ole Winther

Ole Winther

Professor

DTU COMPUTE
Department of Applied Mathematics and Computer Science

Technical University of Denmark

Richard Petersens Plads

Building 321, room 115

2800 Kgs. Lyngby

Ph.
Fax +45 45 87 25 99
E-mail olwi@dtu.dk
ORCID 0000-0002-1966-3205
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2019
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Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data

Kinalis, Savvas ; Nielsen, Finn Cilius ; Winther, Ole ; Bagger, Frederik Otzen
in: BMC Bioinformatics, vol: 20, issue: 1

Type: Journal article (Peer reviewed)

Status: Published     |    Year: 2019     |    DOI: https://doi.org/10.1186/s12859-019-2952-9

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Detecting sequence signals in targeting peptides using deep learning

Armenteros, Jose Juan Almagro ; Salvatore, Marco ; Emanuelsson, Olof ; Winther, Ole ; Von Heijne, Gunnar ; Elofsson, Arne ; Nielsen, Henrik
in: Life Science Alliance, vol: 2, issue: 5

Type: Journal article (Peer reviewed)

Status: Published     |    Year: 2019     |    DOI: https://doi.org/10.26508/lsa.201900429

 

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning

Klausen, Michael Schantz ; Jespersen, Martin Closter ; Nielsen, Henrik ; Jensen, Kamilla Kjærgaard ; Jurtz, Vanessa Isabell ; Sønderby, Casper Kaae ; Sommer, Morten Otto Alexander ; Winther, Ole ; Nielsen, Morten ; Petersen, Bent ; Marcatili, Paolo
in: Proteins: Structure, Function, and Bioinformatics, vol: 87, issue: 6, pages: 520-527

Type: Journal article (Peer reviewed)

Status: Published     |    Year: 2019     |    DOI: https://doi.org/10.1002/prot.25674

2018
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Bayesian structure learning for dynamic brain connectivity

Andersen, Michael Riis ; Winther, Ole ; Hansen, Lars Kai ; Poldrack, Russell ; Koyejo, Oluwasanmi
part of: Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, pages: 1436-1446, 2019
Presented at:
21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018

Type: Article in proceedings (Peer reviewed)

Status: Published     |    Year: 2018

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A Deep Learning Approach to Identify Local Structures in Atomic-Resolution Transmission Electron Microscopy Images

Madsen, Jacob ; Liu, Pei ; Kling, Jens ; Wagner, Jakob Birkedal ; Hansen, Thomas Willum ; Winther, Ole ; Schiøtz, Jakob
in: Advanced Theory and Simulations, vol: 1, issue: 8

Type: Journal article (Peer reviewed)

Status: Published     |    Year: 2018     |    DOI: https://doi.org/10.1002/adts.201800037

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Deep Generative Models for Molecular Science

Jørgensen, Peter Bjørn ; Schmidt, Mikkel Nørgaard ; Winther, Ole
in: Molecular Informatics, vol: 37, issue: 1-2

Type: Journal article (Peer reviewed)

Status: Published     |    Year: 2018     |    DOI: https://doi.org/10.1002/minf.201700133

 

Deep learning for automated drivetrain fault detection

Bach-Andersen, Martin ; Rømer-Odgaard, Bo ; Winther, Ole
in: Wind Energy, vol: 21, issue: 1, pages: 29-41

Type: Journal article (Peer reviewed)

Status: Published     |    Year: 2018     |    DOI: https://doi.org/10.1002/we.2142

 

Recurrent Relational Networks

Palm, Rasmus Berg ; Paquet, Ulrich ; Winther, Ole
part of: 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 2019
Presented at:
32nd Conference on Neural Information Processing Systems

Type: Article in proceedings (Peer reviewed)

Status: Published     |    Year: 2018

2017
 

A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

Fraccaro, Marco ; Kamronn, Simon Due ; Paquet, Ulrich ; Winther, Ole
part of: Proceedings of 31st Conference on Neural Information Processing Systems , 2017
Presented at:
31st Conference on Neural Information Processing Systems

Type: Article in proceedings (Peer reviewed)

Status: Published     |    Year: 2017