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Günter Klambauer
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2020 – today
- 2024
- [j16]Emma Svensson
, Pieter-Jan Hoedt, Sepp Hochreiter, Günter Klambauer
HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions. J. Chem. Inf. Model. 64(7): 2539-2553 (2024) - [j15]Philipp Renz
Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators. J. Chem. Inf. Model. 64(15): 5756-5761 (2024) - [c15]Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak, Johannes Brandstetter, Günter Klambauer, Andreas Mayr:
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks. Tiny Papers @ ICLR 2024 - [c14]Ghadi S. Al Hajj, Aliaksandr Hubin, Chakravarthi Kanduri, Milena Pavlovic, Knut Dagestad Rand, Michael Widrich, Anne H. Schistad Solberg, Victor Greiff, Johan Pensar, Günter Klambauer, Geir Kjetil Sandve:
Incorporating probabilistic domain knowledge into deep multiple instance learning. ICML 2024 - [c13]Emese Sükei, Elisabeth Rumetshofer, Niklas Schmidinger, Andreas Mayr, Ursula Schmidt-Erfurth, Günter Klambauer, Hrvoje Bogunovic:
Improving Clinical Predictions with Multi-Modal Pre-training in Retinal Imaging. ISBI 2024: 1-5 - [i24]Lisa Schneckenreiter, Richard Freinschlag, Florian Sestak, Johannes Brandstetter, Günter Klambauer, Andreas Mayr:
GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks. CoRR abs/2403.04747 (2024) - [i23]Florian Sestak, Lisa Schneckenreiter, Johannes Brandstetter, Sepp Hochreiter, Andreas Mayr, Günter Klambauer:
VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification. CoRR abs/2404.07194 (2024) - [i22]Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter:
xLSTM: Extended Long Short-Term Memory. CoRR abs/2405.04517 (2024) - 2023
- [c12]Johannes Schimunek, Philipp Seidl, Lukas Friedrich, Daniel Kuhn, Friedrich Rippmann, Sepp Hochreiter, Günter Klambauer:
Context-enriched molecule representations improve few-shot drug discovery. ICLR 2023 - [c11]Philipp Seidl, Andreu Vall, Sepp Hochreiter, Günter Klambauer:
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language. ICML 2023: 30458-30490 - [c10]Pieter-Jan Hoedt, Günter Klambauer:
Principled Weight Initialisation for Input-Convex Neural Networks. NeurIPS 2023 - [c9]Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Günter Klambauer, Sepp Hochreiter:
Quantification of Uncertainty with Adversarial Models. NeurIPS 2023 - [i21]Philipp Seidl, Andreu Vall, Sepp Hochreiter, Günter Klambauer:
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language. CoRR abs/2303.03363 (2023) - [i20]Johannes Schimunek, Philipp Seidl, Lukas Friedrich, Daniel Kuhn, Friedrich Rippmann, Sepp Hochreiter, Günter Klambauer:
Context-enriched molecule representations improve few-shot drug discovery. CoRR abs/2305.09481 (2023) - [i19]Kajetan Schweighofer, Lukas Aichberger, Mykyta Ielanskyi, Günter Klambauer, Sepp Hochreiter:
Quantification of Uncertainty with Adversarial Models. CoRR abs/2307.03217 (2023) - [i18]Pieter-Jan Hoedt, Günter Klambauer:
Principled Weight Initialisation for Input-Convex Neural Networks. CoRR abs/2312.12474 (2023) - 2022
- [j14]Philipp Seidl
Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks. J. Chem. Inf. Model. 62(9): 2111-2120 (2022) - [j13]Theresa Roland
Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. J. Medical Syst. 46(5): 23 (2022) - [j12]Philippe A. Robert
Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction. Nat. Comput. Sci. 2(12): 845-865 (2022) - [c8]Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner, Viet T. Tran, Fei Tang, Hubert Ramsauer, David P. Kreil, Michael Kopp, Günter Klambauer, Angela Bitto, Sepp Hochreiter:
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP. NeurIPS 2022 - 2021
- [j11]Andreu Vall, Yogesh Sabnis, Jiye Shi, Reiner Class, Sepp Hochreiter, Günter Klambauer:
The Promise of AI for DILI Prediction. Frontiers Artif. Intell. 4: 638410 (2021) - [j10]Rocío Mercado
Graph networks for molecular design. Mach. Learn. Sci. Technol. 2(2): 25023 (2021) - [j9]Milena Pavlovic
The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat. Mach. Intell. 3(11): 936-944 (2021) - [c7]Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich, Lukas Gruber, Markus Holzleitner, Thomas Adler, David P. Kreil, Michael K. Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter:
Hopfield Networks is All You Need. ICLR 2021 - [c6]Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer:
MC-LSTM: Mass-Conserving LSTM. ICML 2021: 4275-4286 - [i17]Pieter-Jan Hoedt, Frederik Kratzert, Daniel Klotz, Christina Halmich, Markus Holzleitner, Grey Nearing, Sepp Hochreiter, Günter Klambauer:
MC-LSTM: Mass-Conserving LSTM. CoRR abs/2101.05186 (2021) - [i16]Philipp Seidl, Philipp Renz, Natalia Dyubankova, Paulo Neves, Jonas Verhoeven, Jörg K. Wegner
Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction. CoRR abs/2104.03279 (2021) - [i15]Andreas Fürst, Elisabeth Rumetshofer, Viet Tran, Hubert Ramsauer, Fei Tang, Johannes Lehner, David P. Kreil, Michael Kopp
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP. CoRR abs/2110.11316 (2021) - 2020
- [j8]Noé Sturm, Andreas Mayr, Thanh Le Van, Vladimir I. Chupakhin
Industry-scale application and evaluation of deep learning for drug target prediction. J. Cheminformatics 12(1): 26 (2020) - [c5]Michael Widrich, Bernhard Schäfl, Milena Pavlovic, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer:
Modern Hopfield Networks and Attention for Immune Repertoire Classification. NeurIPS 2020 - [i14]Markus Hofmarcher, Andreas Mayr, Elisabeth Rumetshofer, Peter Ruch, Philipp Renz, Johannes Schimunek, Philipp Seidl, Andreu Vall, Michael Widrich, Sepp Hochreiter, Günter Klambauer:
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks. CoRR abs/2004.00979 (2020) - [i13]Michael Widrich
Modern Hopfield Networks and Attention for Immune Repertoire Classification. CoRR abs/2007.13505 (2020) - [i12]Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner, Philipp Seidl, Michael Widrich
Hopfield Networks is All You Need. CoRR abs/2008.02217 (2020) - [i11]Thomas Adler, Johannes Brandstetter, Michael Widrich
Cross-Domain Few-Shot Learning by Representation Fusion. CoRR abs/2010.06498 (2020) - [i10]Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Günter Klambauer, Sepp Hochreiter, Grey Nearing:
Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling. CoRR abs/2012.14295 (2020)
2010 – 2019
- 2019
- [j7]Günter Klambauer
Machine Learning in Drug Discovery. J. Chem. Inf. Model. 59(3): 945-946 (2019) - [j6]Noé Sturm
Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models. J. Chem. Inf. Model. 59(3): 962-972 (2019) - [j5]Markus Hofmarcher, Elisabeth Rumetshofer
Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks. J. Chem. Inf. Model. 59(3): 1163-1171 (2019) - [c4]Elisabeth Rumetshofer
Human-level Protein Localization with Convolutional Neural Networks. ICLR (Poster) 2019 - [p4]Markus Hofmarcher, Thomas Unterthiner
Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation. Explainable AI 2019: 285-296 - [p3]Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter
Interpretable Deep Learning in Drug Discovery. Explainable AI 2019: 331-345 - [p2]Frederik Kratzert, Mathew Herrnegger, Daniel Klotz, Sepp Hochreiter
NeuralHydrology - Interpreting LSTMs in Hydrology. Explainable AI 2019: 347-362 - [i9]Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter
Interpretable Deep Learning in Drug Discovery. CoRR abs/1903.02788 (2019) - [i8]Frederik Kratzert, Mathew Herrnegger, Daniel Klotz, Sepp Hochreiter
NeuralHydrology - Interpreting LSTMs in Hydrology. CoRR abs/1903.07903 (2019) - [i7]Frederik Kratzert, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter
Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling. CoRR abs/1907.08456 (2019) - [i6]Susanne Kimeswenger, Elisabeth Rumetshofer, Markus Hofmarcher, Philipp Tschandl, Harald Kittler, Sepp Hochreiter
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images. CoRR abs/1911.06616 (2019) - 2018
- [j4]Kristina Preuer, Richard P. I. Lewis, Sepp Hochreiter
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinform. 34(9): 1538-1546 (2018) - [j3]Sepp Hochreiter
Machine Learning in Drug Discovery. J. Chem. Inf. Model. 58(9): 1723-1724 (2018) - [j2]Kristina Preuer, Philipp Renz, Thomas Unterthiner
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery. J. Chem. Inf. Model. 58(9): 1736-1741 (2018) - [c3]Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter:
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. ICLR (Poster) 2018 - [i5]Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter
Fréchet ChemblNet Distance: A metric for generative models for molecules. CoRR abs/1803.09518 (2018) - 2017
- [c2]Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Self-Normalizing Neural Networks. NIPS 2017: 971-980 - [i4]Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Self-Normalizing Neural Networks. CoRR abs/1706.02515 (2017) - [i3]Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, Sepp Hochreiter
GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium. CoRR abs/1706.08500 (2017) - [i2]Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. CoRR abs/1708.08819 (2017) - 2015
- [j1]Günter Klambauer
Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map. Bioinform. 31(20): 3392-3394 (2015) - [i1]Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Sepp Hochreiter
Toxicity Prediction using Deep Learning. CoRR abs/1503.01445 (2015) - 2014
- [b1]Günter Klambauer:
Machine Learning Techniques for the Analysis of High-Throughput DNA and RNA Sequencing Data. Universität Linz, 2014, pp. I-XXII, 1-184 - [p1]Günter Klambauer:
Techniken des maschinellen Lernens zur Analyse von Hochdurchsatz-DNA- und RNA-Sequenzierungsdaten. Ausgezeichnete Informatikdissertationen 2014: 131-140 - 2012
- [c1]Tor Johan Mikael Karlsson, Óscar Torreño Tirado, Daniel Ramet Barea, Günter Klambauer
Enabling Large-Scale Bioinformatics Data Analysis with Cloud Computing. ISPA 2012: 640-645
Coauthor Index
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last updated on 2024-10-31 21:07 CET by the dblp team
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