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Institute for Machine Learning
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Publications 2018

Journals

Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery Large-scale comparison of machine learning methods for drug target prediction on ChEMBL  Chem. Sci Multivariate analytics of chromatographic data: Visual computing based on moving window factor models J. Chromatogr. B. Defining objective clusters for rabies virus sequences using affinity propagation clustering PLoS Neglect. Trop. Dis.

Conferences

End-to-End Learning of Pharmacological Assays from High-resolution Microscopy Images NIPS
Author(s): Markus Hofmarcher, Elisabeth Rumetshofer, Markus Holzleitner, Bernhard Schäfl, Sepp Hochreiter, Günter Klambauer
Published:

Women in Machine Learning Workshop, Conference Neural Information Processing Systems Foundation (NIPS 2018), December 3-8, 2018, Montreal, Canada

Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery NIPS
Author(s):

Kristina Preuer, Philipp Renz, Thomas Unterthiner, Johannes Kofler, Sepp Hochreiter, Günter Klambauer

Published:

Women in Machine Learning Workshop, Conference Neural Information Processing Systems Foundation (NIPS 2018), December 3-8, 2018, Montreal, Canada

Interpretable Deep Learning in Drug Development NIPS
Author(s):

Kristina Preuer, Johannes Brandstetter, Günter Klambauer,Dr. Sepp Hochreiter, Thomas Unterthiner

Published:

Women in Machine Learning Workshop, Conference Neural Information Processing Systems Foundation (NIPS 2018), December 3-8, 2018, Montreal, Canada

Characterising Activation Functions by Their Backward Dynamics Around Forward Fixed Points NIPS
Author(s):

Pieter-Jan Hoedt, Sepp Hochreiter, Günter Klambauer

Published:

Conference Neural Information Processing Systems Foundation (NIPS 2018), December 3-8, 2018, Montreal, Canada

GPU-driven Deep Learning Paves New Ways for Drug Discovery Through High-Content Imaging GTC
Author(s): Maciej Kandula, Elisabeth Rumetshofer, Markus Hofmarcher, Günter Klambauer, Sepp Hochreiter
Published: Poster at GPU Technology Conference 2018, October 9-11, 2018, Munich, Germany (Best Paper Award)
First Order Generative Adversarial Networks ICML
Author(s): Calvin Seward, Thomas Unterthiner, Urs Bergmann, Nikolay Jetchev, Sepp Hochreiter
Published: International Conference on Machine Learning, July 10-15, 2018, Stockholm, Sweden
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields ICLR
Author(s): Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter
Published: International Conference on Learning Representations, April 30 - May 03, 2018, Vancouver, Canada
Making Sense of Very Rare and Private Single-Nucleotide Variants MAQC
Author(s): Ulrich Bodenhofer, Sepp Hochreiter
Published: MAQC Society Second Annual Meeting, February 24-27, 2018, Shanghai, China

Publications (non-peer-reviewed)

First Order Generative Adversarial Networks arXiv.org RUDDER: Return Decomposition for Delayed Rewards arXiv.org Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields arXiv.org