Enabling Large-Scale Bioinformatics Data Analysis with Cloud Computing. - Tor Johan Mikael Karlsson; Óscar Torreño Tirado; Daniel Ramet Barea; Günter Klambauer; Miriam Cano Castillo; Oswaldo Trelles (ISPA, 2012)

Machine Learning Techniques for the Analysis of High-Throughput DNA and RNA Sequencing Data. - Günter Klambauer (2014)

Techniken des maschinellen Lernens zur Analyse von Hochdurchsatz-DNA- und RNA-Sequenzierungsdaten. - Günter Klambauer (Ausgezeichnete Informatikdissertationen, 2014)

Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map. - Günter Klambauer; Martin Wischenbart; Michael Mahr; Thomas Unterthiner; Andreas Mayr; Sepp Hochreiter (Bioinform., 2015)

Toxicity Prediction using Deep Learning. - Thomas Unterthiner; Andreas Mayr; Günter Klambauer; Sepp Hochreiter (CoRR, 2015)

Self-Normalizing Neural Networks. - Günter Klambauer; Thomas Unterthiner; Andreas Mayr; Sepp Hochreiter (NIPS, 2017)

GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium. - Martin Heusel; Hubert Ramsauer; Thomas Unterthiner; Bernhard Nessler; Günter Klambauer; Sepp Hochreiter (CoRR, 2017)

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. - Thomas Unterthiner; Bernhard Nessler; Günter Klambauer; Martin Heusel; Hubert Ramsauer; Sepp Hochreiter (CoRR, 2017)

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields. - Thomas Unterthiner; Bernhard Nessler; Calvin Seward; Günter Klambauer; Martin Heusel; Hubert Ramsauer; Sepp Hochreiter (ICLR (Poster), 2018)

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. - Kristina Preuer; Richard P. I. Lewis; Sepp Hochreiter; Andreas Bender 002; Krishna C. Bulusu; Günter Klambauer (Bioinform., 2018)

Fréchet ChemblNet Distance: A metric for generative models for molecules. - Kristina Preuer; Philipp Renz; Thomas Unterthiner; Sepp Hochreiter; Günter Klambauer (CoRR, 2018)

Machine Learning in Drug Discovery. - Sepp Hochreiter; Günter Klambauer; Matthias Rarey (J. Chem. Inf. Model., 2018)

Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery. - Kristina Preuer; Philipp Renz; Thomas Unterthiner; Sepp Hochreiter; Günter Klambauer (J. Chem. Inf. Model., 2018)

Human-level Protein Localization with Convolutional Neural Networks. - Elisabeth Rumetshofer; Markus Hofmarcher; Clemens Röhrl; Sepp Hochreiter; Günter Klambauer (ICLR (Poster), 2019)

Interpretable Deep Learning in Drug Discovery. - Kristina Preuer; Günter Klambauer; Friedrich Rippmann; Sepp Hochreiter; Thomas Unterthiner (CoRR, 2019)

NeuralHydrology - Interpreting LSTMs in Hydrology. - Frederik Kratzert; Mathew Herrnegger; Daniel Klotz; Sepp Hochreiter; Günter Klambauer (CoRR, 2019)

Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling. - Frederik Kratzert; Daniel Klotz; Guy Shalev; Günter Klambauer; Sepp Hochreiter; Grey Nearing (CoRR, 2019)

Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images. - Susanne Kimeswenger; Elisabeth Rumetshofer; Markus Hofmarcher; Philipp Tschandl; Harald Kittler; Sepp Hochreiter; Wolfram Hötzenecker; Günter Klambauer (CoRR, 2019)

Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks. - Markus Hofmarcher; Elisabeth Rumetshofer; Djork-Arné Clevert; Sepp Hochreiter; Günter Klambauer (J. Chem. Inf. Model., 2019)

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models. - Noé Sturm; Jiangming Sun; Yves Vandriessche; Andreas Mayr; Günter Klambauer; Lars Carlsson; Ola Engkvist; Hongming Chen 001 (J. Chem. Inf. Model., 2019)

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation. - Markus Hofmarcher; Thomas Unterthiner; Jose A. Arjona-Medina; Günter Klambauer; Sepp Hochreiter; Bernhard Nessler (Explainable AI, 2019)

NeuralHydrology - Interpreting LSTMs in Hydrology. - Frederik Kratzert; Mathew Herrnegger; Daniel Klotz; Sepp Hochreiter; Günter Klambauer (Explainable AI, 2019)

Interpretable Deep Learning in Drug Discovery. - Kristina Preuer; Günter Klambauer; Friedrich Rippmann; Sepp Hochreiter; Thomas Unterthiner (Explainable AI, 2019)

Modern Hopfield Networks and Attention for Immune Repertoire Classification. - Michael Widrich; Bernhard Schäfl; Milena Pavlovic; Hubert Ramsauer; Lukas Gruber; Markus Holzleitner; Johannes Brandstetter; Geir Kjetil Sandve; Victor Greiff; Sepp Hochreiter; Günter Klambauer (NeurIPS, 2020)

Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks. - Markus Hofmarcher; Andreas Mayr; Elisabeth Rumetshofer; Peter Ruch; Philipp Renz; Johannes Schimunek; Philipp Seidl; Andreu Vall; Michael Widrich; Sepp Hochreiter; Günter Klambauer (CoRR, 2020)

Hopfield Networks is All You Need. - Hubert Ramsauer; Bernhard Schäfl; Johannes Lehner; Philipp Seidl; Michael Widrich; Lukas Gruber; Markus Holzleitner; Milena Pavlovic; Geir Kjetil Sandve; Victor Greiff; David P. Kreil; Michael Kopp 001; et al. (CoRR, 2020)

Cross-Domain Few-Shot Learning by Representation Fusion. - Thomas Adler; Johannes Brandstetter; Michael Widrich; Andreas Mayr; David P. Kreil; Michael Kopp 001; Günter Klambauer; Sepp Hochreiter (CoRR, 2020)

Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling. - Daniel Klotz; Frederik Kratzert; Martin Gauch; Alden Keefe Sampson; Günter Klambauer; Sepp Hochreiter; Grey Nearing (CoRR, 2020)

Industry-scale application and evaluation of deep learning for drug target prediction. - Noé Sturm; Andreas Mayr; Thanh Le Van; Vladimir I. Chupakhin; Hugo Ceulemans; Jörg K. Wegner; José Felipe Golib Dzib; Nina Jeliazkova; Yves Vandriessche; Stanislav Böhm; Vojtech Cima; Jan Martinovic; et al. (J. Cheminformatics, 2020)

Hopfield Networks is All You Need. - Hubert Ramsauer; Bernhard Schäfl; Johannes Lehner; Philipp Seidl; Michael Widrich; Lukas Gruber; Markus Holzleitner; Thomas Adler; David P. Kreil; Michael K. Kopp 001; Günter Klambauer; Johannes Brandstetter; et al. (ICLR, 2021)

MC-LSTM: Mass-Conserving LSTM. - Pieter-Jan Hoedt; Frederik Kratzert; Daniel Klotz; Christina Halmich; Markus Holzleitner; Grey Nearing; Sepp Hochreiter; Günter Klambauer (ICML, 2021)

Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction. - Philipp Seidl; Philipp Renz; Natalia Dyubankova; Paulo Neves; Jonas Verhoeven; Jörg K. Wegner; Sepp Hochreiter; Günter Klambauer (CoRR, 2021)

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP. - Andreas Fürst; Elisabeth Rumetshofer; Viet Tran; Hubert Ramsauer; Fei Tang; Johannes Lehner; David P. Kreil; Michael Kopp 001; Günter Klambauer; Angela Bitto-Nemling; Sepp Hochreiter (CoRR, 2021)

The Promise of AI for DILI Prediction. - Andreu Vall; Yogesh Sabnis; Jiye Shi; Reiner Class; Sepp Hochreiter; Günter Klambauer (Frontiers Artif. Intell., 2021)

Graph networks for molecular design. - Rocío Mercado; Tobias Rastemo; Edvard Lindelöf; Günter Klambauer; Ola Engkvist; Hongming Chen 001; Esben Jannik Bjerrum (Mach. Learn. Sci. Technol., 2021)

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. - Milena Pavlovic; 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; et al. (Nat. Mach. Intell., 2021)

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP. - Andreas Fürst; Elisabeth Rumetshofer; Johannes Lehner; Viet T. Tran; Fei Tang; Hubert Ramsauer; David P. Kreil; Michael Kopp 001; Günter Klambauer; Angela Bitto; Sepp Hochreiter (NeurIPS, 2022)

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks. - Philipp Seidl; Philipp Renz; Natalia Dyubankova; Paulo Neves; Jonas Verhoeven; Jörg K. Wegner; Marwin H. S. Segler; Sepp Hochreiter; Günter Klambauer (J. Chem. Inf. Model., 2022)

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. - Theresa Roland; Carl Böck; Thomas Tschoellitsch; Alexander Maletzky; Sepp Hochreiter; Jens Meier; Günter Klambauer (J. Medical Syst., 2022)

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction. - Philippe A. Robert; Rahmad Akbar; Robert Frank 006; Milena Pavlovic; Michael Widrich; Igor Snapkov; Andrei Slabodkin; Maria Chernigovskaya; Lonneke Scheffer; Eva Smorodina; Puneet Rawat; Brij Bhushan Mehta; et al. (Nat. Comput. Sci., 2022)

Context-enriched molecule representations improve few-shot drug discovery. - Johannes Schimunek; Philipp Seidl; Lukas Friedrich; Daniel Kuhn; Friedrich Rippmann; Sepp Hochreiter; Günter Klambauer (ICLR, 2023)

Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language. - Philipp Seidl; Andreu Vall; Sepp Hochreiter; Günter Klambauer (ICML, 2023)

Principled Weight Initialisation for Input-Convex Neural Networks. - Pieter-Jan Hoedt; Günter Klambauer (NeurIPS, 2023)

Quantification of Uncertainty with Adversarial Models. - Kajetan Schweighofer; Lukas Aichberger; Mykyta Ielanskyi; Günter Klambauer; Sepp Hochreiter (NeurIPS, 2023)

GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks. - Lisa Schneckenreiter; Richard Freinschlag; Florian Sestak; Johannes Brandstetter; Günter Klambauer; Andreas Mayr (Tiny Papers @ ICLR, 2024)

Incorporating probabilistic domain knowledge into deep multiple instance learning. - 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 (ICML, 2024)

Improving Clinical Predictions with Multi-Modal Pre-training in Retinal Imaging. - Emese Sükei; Elisabeth Rumetshofer; Niklas Schmidinger; Andreas Mayr; Ursula Schmidt-Erfurth; Günter Klambauer; Hrvoje Bogunovic (ISBI, 2024)

xLSTM: Extended Long Short-Term Memory. - Maximilian Beck; Korbinian Pöppel; Markus Spanring; Andreas Auer; Oleksandra Prudnikova; Michael Kopp 001; Günter Klambauer; Johannes Brandstetter; Sepp Hochreiter (NeurIPS, 2024)

VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification. - Florian Sestak; Lisa Schneckenreiter; Johannes Brandstetter; Sepp Hochreiter; Andreas Mayr; Günter Klambauer (CoRR, 2024)

A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks. - Thomas Schmied; Thomas Adler; Vihang Patil; Maximilian Beck; Korbinian Pöppel; Johannes Brandstetter; Günter Klambauer; Razvan Pascanu; Sepp Hochreiter (CoRR, 2024)

Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators. - Philipp Renz; Sohvi Luukkonen; Günter Klambauer (J. Chem. Inf. Model., 2024)

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions. - Emma Svensson; Pieter-Jan Hoedt; Sepp Hochreiter; Günter Klambauer (J. Chem. Inf. Model., 2024)

Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences. - Niklas Schmidinger; Lisa Schneckenreiter; Philipp Seidl; Johannes Schimunek; Pieter-Jan Hoedt; Johannes Brandstetter; Andreas Mayr; Sohvi Luukkonen; Sepp Hochreiter; Günter Klambauer (ICLR, 2025)

Advancing OCT-Based Retinal Disease Classification with XLSTM: A Framework for Variable-Length Volume Processing. - Emese Sükei; Marzieh Oghbaie; Ursula Schmidt-Erfurth; Günter Klambauer; Hrvoje Bogunovic (ISBI, 2025)

MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery. - Johannes Schimunek; Sohvi Luukkonen; Günter Klambauer (J. Chem. Inf. Model., 2025)

LaM-SLidE: Latent Space Modeling of Spatial Dynamical Systems via Linked Entities. - Florian Sestak; Artur P. Toshev; Andreas Fürst; Günter Klambauer; Andreas Mayr; Johannes Brandstetter (CoRR, 2025)

xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference. - Maximilian Beck; Korbinian Pöppel; Phillip Lippe; Richard Kurle; Patrick M. Blies; Günter Klambauer; Sebastian Böck; Sepp Hochreiter (CoRR, 2025)

TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning. - Andreas Auer; Patrick Podest; Daniel Klotz; Sebastian Böck; Günter Klambauer; Sepp Hochreiter (CoRR, 2025)

Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data. - Robert Frank 006; Michael Widrich; Rahmad Akbar; Günter Klambauer; Geir Kjetil Sandve; Philippe A. Robert; Victor Greiff (CoRR, 2025)