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.

Publikationen 2021

Zeitschriften

Quantum Optical Experiments Modeled by Long Short-Term Memory Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER
Author(s): Markus Holzleitner, Lukas Gruber, José Arjona-Medina, Johannes Brandstetter, Sepp Hochreiter
Published: Transactions on Large-Scale Data-and Knowledge-Centered Systems XLVIII, 2020, pp. 105–130, Springer, Berlin, Heidelberg
Uncertainty Estimation with Deep Learning for Rainfall–Runoff Modelling Machine learning-based risk profile classification of patients undergoing elective heart valve surgery Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network A note on leveraging synergy in multiple meteorological datasets with deep learning for rainfall-runoff modeling The Promise of AI for DILI Prediction Artificial neural networks and pathologists recognize basal cell carcinomas based on different histological patterns

Konferenzen

Learning 3D Granular Flow Simulations
Author(s): Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter
Published: Accepted at Deep Learning for Simulation (SimDL) Workshop @ ICLR 2021 (online), arxiv.org/abs/2105.01636, öffnet eine externe URL in einem neuen Fenster
Poster: simdl.github.io/posters/42-supp_poster.pdf, öffnet eine externe URL in einem neuen Fenster
Contributed Talk: slideslive.de/38955315/learning-3d-granular-flow-simulations, öffnet eine externe URL in einem neuen Fenster
Benchmarking recent Deep Learning methods on the extended Tox21 data set
Author(s): Philipp Seidl, Christina Halmich, Andreas Mayr, Andreu Vall, Peter Ruch, Sepp Hochreiter, Günter Klambauer
Published: 19th International Workshop on (Q)SAR in Environmental and Health Sciences (QSAR2021), Poster Session, June 2021, online
Comparative assessment of interpretability methods of deep activity models for hERG
Author(s): Johannes Schimunek, Lukas Friedrich, Daniel Kuhn, Sepp Hochreiter, Friedrich Rippmann, Günter Klambauer
Published: 19th International Workshop on (Q)SAR in Environmental and Health Sciences (QSAR2021), Poster Session, June 2021, online
Large-scale river network modeling using Graph Neural Networks
Author(s): Daniel Klotz, Martin Gauch, Christoph Klingler, Grey Nearing, Sepp Hochreiter
Published: Video/Presentation at EGU General Assembly 2021, April 2021 (online)
Multi-Timescale LSTM for Rainfall–Runoff Forecasting
Author(s): Frederik Kratzert, Grey Nearing, Jimmy Lin, Sepp Hochreiter, Johannes Brandstetter, Daniel Klotz
Published: Video/Presentation at EGU General Assembly 2021, April 2021 (online)
Uncertainty estimation with LSTM based rainfall-runoff models
Author(s): Frederik Kratzert, Martin Gauch, Alden K. Sampson, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter, Grey Nearing
Published: Video/Presentation at EGU General Assembly 2021, April 2021 (online)
LamaH: Large-sample Data for Hydrology in Central Europe
Author(s): Christoph Klingler, Mathew Herrnegger, Frederik Kratzert, Karsten Schulz
Published: Video/Presentation at EGU General Assembly 2021, April 2021 (online)

Publikationen (non-peer-reviewed)

Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP Machine Learning based COVID-19 Diagnosis from Blood Tests with Robustness to Domain ShiftsmedRxiv Modern Hopfield Networks for Few- and Zero-Shot Reaction PredictionarXiv.org MC-LSTM: Mass-Conserving LSTMarXiv.org Trusted Artificial Intelligence: Towards Certification of Machine Learning ApplicationsarXiv.org immuneML: an Ecosystem for Machine Learning Analysis of Adaptive Immune Receptor RepertoiresbioRxiv.org
Author(s): Milena Pavlović, Lonneke Scheffer,Keshav Motwani, Chakravarthi Kanduri, Radmila Kompova, Nikolay Vazov, Knut Waagan, Fabian L. M. Bernal, Alexandre Almeida Costa, Brian Corrie, Rahmad Akbar, Ghadi S. Al Hajj, Gabriel Balaban, Todd M. Brusko, Maria Chernigovskaya, Scott Christley, Lindsay G. Cowell, Robert Frank, Ivar Grytten, Sveinung Gundersen, Ingrid Hobæk Haff, Sepp Hochreiter, Eivind Hovig, Ping-Han Hsieh, Günter Klambauer, Marieke L. Kuijjer, Christin Lund-Andersen, Antonio Martini, Thomas Minotto, Johan Pensar, Knut Rand, Enrico Riccardi, Philippe A. Robert, Artur Rocha, Andrei Slabodkin, Igor Snapkov, Ludvig M. Sollid, Dmytro Titov, Cédric R. Weber, Michael Widrich, Gur Yaari, Victor Greiff, Geir Kjetil Sandve
Published:

bioRxiv, 2021 DOI: 10.1101/2021.03.08.433891, öffnet eine externe URL in einem neuen Fenster

Boundary Graph Neural Networks for 3D SimulationsarXiv.org In silico proof of principle of machine learning-based antibody design at unconstrained scalebioXiv.org
Author(s):

Rahmad Akbar, Philippe A. Robert, Cédric R. Weber, Michael Widrich, Robert Frank, Milena Pavlović, Lonneke Scheffer, Maria Chernigovskaya, Igor Snapkov, Andrei Slabodkin, Brij Bhushan Mehta, Enkelejda Miho, Fridtjof Lund-Johansen, Jan Terje Andersen, Sepp Hochreiter, Ingrid Hobæk Haff, Günter Klambauer, Geir Kjetil Sandve, Victor Greiff

Published:

bioRxiv, 2021 doi: 10.1101/2021.07.08.451480, öffnet eine externe URL in einem neuen Fenster