Go to JKU Homepage
Institute for Machine Learning
What's that?

Institutes, schools, other departments, and programs create their own web content and menus.

To help you better navigate the site, see here where you are at the moment.

Publications 2020
 

Journals

Hsa-miR-375/RASD1 signaling may predict local control in early breast cancer Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns 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:

J Cheminform 12, 26, 2020 https://doi.org/10.1186/s13321-020-00428-5, opens an external URL in a new window

Machine learning–based prediction of transfusion What Role Does Hydrological Science Play in the Age of Machine Learning? On failure modes in molecule generation and optimization

Conferences

A Deep Learning Architecture for Conservative Dynamical Systems: Application to Rainfall-Runoff Modeling
Author(s): Grey Nearing, Frederik Kratzert, Daniel Klotz, Pieter-Jan Hoedt, Günter Klambauer, Sepp Hochreiter, Hoshin Gupta, Sella Nevo, Yossi Matias
Published:

NeurIPS Workshop: AI for Earth Sciences, Conference Neural Information Processing Systems Foundation (NeurIPS 2020), December 2020

A Machine Learner’s Guide to Streamflow Prediction
Author(s): Martin Gauch, Daniel Klotz, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, Jimmy Lin
Published:

NeurIPS Workshop: AI for Earth Sciences, Conference Neural Information Processing Systems Foundation (NeurIPS 2020), December 2020

Modern Hopfield Networks and Attention for Immune Repertoire Classification
Author(s): Michael Widrich, Bernhard Schäfl, Milena Pavlovic, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer
Published: Neural Information Processing Systems Foundation (NeurIPS 2020), December 2020
Examining the uncertainty estimation properties of LSTM based rainfall-runoff models
Author(s): Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Sepp Hochreiter, Grey Stephen Nearing
Published: AGU Fall Meeting 2020, 1-17 December, 2020
LSTM-Based Rainfall–Runoff Modeling at Arbitrary Time Scales
Author(s): Martin Gauch, Frederik Kratzert, Daniel Klotz, Grey Stephen Nearing, Jimmy Lin
Published: AGU Fall Meeting 2020, 1-17 December, 2020
What is the role of hydrological science in the age of machine learning?
Author(s): Grey Stephen Nearing, Frederik Kratzert, Alden Keefe Sampson, Craig Pelissier, Daniel Klotz, Cristina Prieto, Jonathan M Frame, Hoshin Gupta
Published: AGU Fall Meeting 2020, 1-17 December, 2020
Cost Optimization at Early Stages of Design Using Deep Reinforcement Learning
Author(s): Lorenzo Servadei, Jiapeng Zheng, Jose Arjona Medina, Michael Werner, Volkan Esen, Sepp Hochreiter, Wolfgang Ecker, Robert Wille
Published: Workshop on Machine Learning for CAD (MLCAD), 2020
Learning from mistakes: Online updating for deep learning models The performance of LSTM models from basin to continental scales Towards deep learning based flood forecasting for ungauged basins

Publications (non-peer-reviewed)

DeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning bioRxiv.org Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networksarXiv.org Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory NetworksarXiv.org Cross-Domain Few-Shot Learning by Representation FusionarXiv.org Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution Hopfield Networks is All You Need
Author(s): Hubert Ramsauer,Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Thomas Adler, Lukas Gruber, Markus Holzleitner, Milena Pavlovic, Geir Kjetil Sandve, Victor Greiff, David Kreil, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Dr. Sepp Hochreiter
Published:

arXiv, 2020 https://arxiv.org/abs/2008.02217, opens an external URL in a new window

Modern Hopfield Networks and Attention for Immune Repertoire Classification