Zur JKU Startseite
Institut für Machine Learning
Was ist das?

Institute, Schools und andere Einrichtungen oder Angebote haben einen Webauftritt mit eigenen Inhalten und Menüs.

Um die Navigation zu erleichtern, ist hier erkennbar, wo man sich gerade befindet.

English, switch language
Zur JKU Startseite

Publikationen 2019

Zeitschriften

Towards learning universal, regional, and local hydrologicalbehaviors via machine learning applied tolarge-sample datasets Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
Machine Learning in Drug Discovery Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
 

Konferenzen

On the Convergence of Episodic Reinforcement Learning Algorithms at the Example of RUDDER
Author(s): Markus Holzleitner, Jose A. Arjona-Medina, Marius-Constantin Dinu, Sepp Hochreiter
Published:

Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada
A Two Time-Scale Update Rule Ensuring Convergence of Episodic Reinforcement Learning Algorithms at the Example of RUDDER
Author(s): Markus Holzleitner, Jose A. Arjona-Medina, Marius-Constantin Dinu, Andreu Vall, Lukas Gruber, Sepp Hochreiter
Published:

Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada
Industry-scale Application and Evaluation of Deep Learning for Drug Target Prediction
Author(s): Noé Sturm, Andreas Mayr, Thanh Le Van, Vladimir Chupakhin, Hugo Ceulemans, Joerg Wegner, Jose-Felipe Golib-Dzib, Nina Jeliazkova, Yves Vandriessche, Stanislav Böhm, Vojtech Cima, Jan Martinovic, Nigel Greene, Tom Vander Aa,  Thomas J. Ashby, Sepp Hochreiter, Ola Engkvist, Günter Klambauer, Hongming Chen
Published:

Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada
A GAN based solver idea for derivative-free optimization problems
Author(s): Hubert Ramsauer, Johannes Brandstetter, Michael Gillhofer, Bernhard Schäfl, Sepp Hochreiter
Published:

Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada
A GAN based solver of black-box inverse problems
Author(s): Michael Gillhofer, Hubert Ramsauer, Johannes Brandstetter, Bernhard Schäfl, Sepp Hochreiter
Published:

Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada
Uncertainty Estimation Methods to Support Decision-Making in Early Phases of Drug Discovery
Author(s): Philipp Renz, Thomas Adler, Sepp Hochreiter, Günter Klambauer
Published:

Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada
Patch Refinement: Localized 3D Object Detection LSTM-Designed Quantum Experiments
Author(s): Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, Sepp Hochreiter
Published:

Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada
Using LSTMs for climate change assessment studies on droughts and floods
Author(s): Frederik Kratzert, Daniel Klotz, Johannes Brandstetter, Pieter-Jan Hoedt, Grey Nearing, Sepp Hochreiter
Published:

Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada
RUDDER: Return Decomposition for Delayed Rewards
Author(s): José Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, Sepp Hochreiter
Published:

Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada
Human-level Protein Localization with Convolutional Neural Networks ICLR
Author(s): Elisabeth Rumetshofer, Markus Hofmarcher, Clemens Röhrl, Sepp Hochreiter, Günter Klambauer
Published: International Conference on Learning Representations (ICLR 2019), May 6-9, 2019, New Orleans, US
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
Author(s): Susanne Kimeswenger, Elisabeth Rumetshofer, Markus Hofmarcher, Philipp Tschandl, Harald Kittler, Sepp Hochreiter, Wolfram Hötzenecker, Günter Klambauer
Published:

Machine Learning for Health Workshop, Conference Neural Information Processing Systems Foundation (NeurIPS 2019), December 8-14, 2019, Vancouver, Canada

https://arxiv.org/abs/1911.06616, öffnet eine externe URL in einem neuen Fenster
Towards the quantification of uncertainty for deep learning based rainfall-runoff models EGU
Author(s): Daniel Klotz, Frederik Kratzert, Mathew Herrnegger, Sepp Hochreiter, and Günter Klambauer
Published: Poster at EGU General Assembly 2019, April 7-12, 2019, Vienna, Austria
Using large data sets towards generating a catchment aware hydrological model for global applications EGU
Author(s): Frederik Kratzert, Daniel Klotz, Mathew Herrnegger, Sepp Hochreiter, and Günter Klambauer
Published: Poster at EGU General Assembly 2019, April 7-12, 2019, Vienna, Austria

Bücher/Buch Kapitel

Explaining and Interpreting LSTMs Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Author(s): Leila Arras, José Arjona-Medina, Michael Widrich, Grégoire Montavon, Michael Gillhofer, Klaus-Robert Müller, Sepp Hochreiter, Wojciech Samek
Editor(s): Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller
Published: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019, Part III, pp. 211-238, doi: 10.1007/978-3-030-28954-6, öffnet eine externe URL in einem neuen Fenster
Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Author(s): Markus Hofmarcher, Thomas Unterthiner, José Arjona-Medina, Günter Klambauer, Sepp Hochreiter, Bernhard Nessler
Editor(s): Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller
Published: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019, Part V, pp. 285-296, doi: 10.1007/978-3-030-28954-6, öffnet eine externe URL in einem neuen Fenster
Interpretable Deep Learning in Drug Discovery Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Author(s): Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, Thomas Unterthiner
Editor(s): Wojciech Samek, Grégoire Montavon, Andrea Vedaldi, Lars Kai Hansen, Klaus-Robert Müller
Published: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 2019, Part V, pp. 331-345, doi: 10.1007/978-3-030-28954-6, öffnet eine externe URL in einem neuen Fenster
NeuralHydrology – Interpreting LSTMs in Hydrology Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Zur JKU Startseite

Johannes Kepler Universität Linz

Altenberger Straße 69

4040 Linz, Österreich

Fax +43 732 2468 4929
Kontakt