Günter Klambauer

CoRR, February, 2025

MHNfs: Prompting In-Context Bioactivity Predictions for Low-Data Drug Discovery.

[BibT_eX]

[DOI]

Johannes Schimunek

Sohvi Luukkonen

J. Chem. Inf. Model., 2025

Advancing OCT-Based Retinal Disease Classification with XLSTM: A Framework for Variable-Length Volume Processing.

[BibT_eX]

[DOI]

Emese Sükei

Marzieh Oghbaie

Ursula Schmidt-Erfurth

Hrvoje Bogunovic

Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging, 2025

Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences.

[BibT_eX]

[DOI]

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2024

Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators.

[BibT_eX]

[DOI]

Philipp Renz

Sohvi Luukkonen

J. Chem. Inf. Model., 2024

A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks.

[BibT_eX]

[DOI]

Razvan Pascanu

CoRR, 2024

VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification.

[BibT_eX]

[DOI]

Florian Sestak

Lisa Schneckenreiter

CoRR, 2024

xLSTM: Extended Long Short-Term Memory.

[BibT_eX]

[DOI]

Oleksandra Prudnikova

Michael Kopp

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Improving Clinical Predictions with Multi-Modal Pre-training in Retinal Imaging.

[BibT_eX]

[DOI]

Emese Sükei

Niklas Schmidinger

Ursula Schmidt-Erfurth

Hrvoje Bogunovic

Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024

Incorporating probabilistic domain knowledge into deep multiple instance learning.

[BibT_eX]

[DOI]

Anne H. Schistad Solberg

Proceedings of the Forty-first International Conference on Machine Learning, 2024

GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks.

[BibT_eX]

[DOI]

Lisa Schneckenreiter

Richard Freinschlag

Florian Sestak

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

2023

Quantification of Uncertainty with Adversarial Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Principled Weight Initialisation for Input-Convex Neural Networks.

[BibT_eX]

[DOI]

Pieter-Jan Hoedt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Context-enriched molecule representations improve few-shot drug discovery.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for antibody specificity prediction.

[BibT_eX]

[DOI]

Nat. Comput. Sci., 2022

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.

[BibT_eX]

[DOI]

J. Medical Syst., 2022

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2022

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP.

[BibT_eX]

[DOI]

Andreas Fürst

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.

[BibT_eX]

[DOI]

Nat. Mach. Intell., 2021

Graph networks for molecular design.

[BibT_eX]

[DOI]

Mach. Learn. Sci. Technol., 2021

The Promise of AI for DILI Prediction.

[BibT_eX]

[DOI]

Frontiers Artif. Intell., 2021

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP.

[BibT_eX]

[DOI]

Andreas Fürst

CoRR, 2021

Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction.

[BibT_eX]

[DOI]

CoRR, 2021

MC-LSTM: Mass-Conserving LSTM.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Hopfield Networks is All You Need.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Industry-scale application and evaluation of deep learning for drug target prediction.

[BibT_eX]

[DOI]

Noé Sturm

Thanh Le Van

Vladimir I. Chupakhin

Hugo Ceulemans

Jörg K. Wegner

José Felipe Golib Dzib

J. Cheminformatics, 2020

Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling.

[BibT_eX]

[DOI]

CoRR, 2020

Cross-Domain Few-Shot Learning by Representation Fusion.

[BibT_eX]

[DOI]

Thomas Adler

CoRR, 2020

Hopfield Networks is All You Need.

[BibT_eX]

[DOI]

CoRR, 2020

Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks.

[BibT_eX]

[DOI]

Markus Hofmarcher

CoRR, 2020

Modern Hopfield Networks and Attention for Immune Repertoire Classification.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Interpretable Deep Learning in Drug Discovery.

[BibT_eX]

[DOI]

Proceedings of the Explainable AI: Interpreting, 2019

NeuralHydrology - Interpreting LSTMs in Hydrology.

[BibT_eX]

[DOI]

Proceedings of the Explainable AI: Interpreting, 2019

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation.

[BibT_eX]

[DOI]

Markus Hofmarcher

Thomas Unterthiner

Jose A. Arjona-Medina

Bernhard Nessler

Proceedings of the Explainable AI: Interpreting, 2019

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2019

Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks.

[BibT_eX]

[DOI]

Markus Hofmarcher

Djork-Arné Clevert

J. Chem. Inf. Model., 2019

Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images.

[BibT_eX]

[DOI]

Susanne Kimeswenger

CoRR, 2019

Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling.

[BibT_eX]

[DOI]

CoRR, 2019

NeuralHydrology - Interpreting LSTMs in Hydrology.

[BibT_eX]

[DOI]

CoRR, 2019

Interpretable Deep Learning in Drug Discovery.

[BibT_eX]

[DOI]

CoRR, 2019

Human-level Protein Localization with Convolutional Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery.

[BibT_eX]

[DOI]

J. Chem. Inf. Model., 2018

Machine Learning in Drug Discovery.

[BibT_eX]

[DOI]

Matthias Rarey

J. Chem. Inf. Model., 2018

Fréchet ChemblNet Distance: A metric for generative models for molecules.

[BibT_eX]

[DOI]

CoRR, 2018

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.

[BibT_eX]

[DOI]

Bioinform., 2018

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields.

[BibT_eX]

[DOI]

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields.

[BibT_eX]

[DOI]

CoRR, 2017

GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium.

[BibT_eX]

[DOI]

CoRR, 2017

Self-Normalizing Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2015

Toxicity Prediction using Deep Learning.

[BibT_eX]

[DOI]

CoRR, 2015

Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map.

[BibT_eX]

[DOI]

Bioinform., 2015

2014

Techniken des maschinellen Lernens zur Analyse von Hochdurchsatz-DNA- und RNA-Sequenzierungsdaten.

[BibT_eX]

[DOI]

Proceedings of the Ausgezeichnete Informatikdissertationen 2014, 2014

Machine Learning Techniques for the Analysis of High-Throughput DNA and RNA Sequencing Data.

[BibT_eX]