Günter Klambauer

Orcid: 0000-0003-2861-5552

According to our database1, Günter Klambauer authored at least 52 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.
J. Chem. Inf. Model., 2024

Diverse Hits in De Novo Molecule Design: Diversity-Based Comparison of Goal-Directed Generators.
J. Chem. Inf. Model., 2024

A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks.
CoRR, 2024

xLSTM: Extended Long Short-Term Memory.
CoRR, 2024

VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification.
CoRR, 2024

Improving Clinical Predictions with Multi-Modal Pre-training in Retinal Imaging.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024

Incorporating probabilistic domain knowledge into deep multiple instance learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks.
Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

2023
Quantification of Uncertainty with Adversarial Models.
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.
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.
Proceedings of the International Conference on Machine Learning, 2023

Context-enriched molecule representations improve few-shot drug discovery.
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.
Nat. Comput. Sci., 2022

Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests.
J. Medical Syst., 2022

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks.
J. Chem. Inf. Model., 2022

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP.
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.
Nat. Mach. Intell., 2021

Graph networks for molecular design.
Mach. Learn. Sci. Technol., 2021

The Promise of AI for DILI Prediction.
Frontiers Artif. Intell., 2021

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP.
CoRR, 2021

Modern Hopfield Networks for Few- and Zero-Shot Reaction Prediction.
CoRR, 2021

MC-LSTM: Mass-Conserving LSTM.
Proceedings of the 38th International Conference on Machine Learning, 2021

Hopfield Networks is All You Need.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Industry-scale application and evaluation of deep learning for drug target prediction.
J. Cheminformatics, 2020

Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling.
CoRR, 2020

Cross-Domain Few-Shot Learning by Representation Fusion.
CoRR, 2020

Hopfield Networks is All You Need.
CoRR, 2020

Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks.
CoRR, 2020

Modern Hopfield Networks and Attention for Immune Repertoire Classification.
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.
Proceedings of the Explainable AI: Interpreting, 2019

NeuralHydrology - Interpreting LSTMs in Hydrology.
Proceedings of the Explainable AI: Interpreting, 2019

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation.
Proceedings of the Explainable AI: Interpreting, 2019

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models.
J. Chem. Inf. Model., 2019

Accurate Prediction of Biological Assays with High-Throughput Microscopy Images and Convolutional Networks.
J. Chem. Inf. Model., 2019

Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images.
CoRR, 2019

Benchmarking a Catchment-Aware Long Short-Term Memory Network (LSTM) for Large-Scale Hydrological Modeling.
CoRR, 2019

NeuralHydrology - Interpreting LSTMs in Hydrology.
CoRR, 2019

Interpretable Deep Learning in Drug Discovery.
CoRR, 2019

Human-level Protein Localization with Convolutional Neural Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery.
J. Chem. Inf. Model., 2018

Machine Learning in Drug Discovery.
J. Chem. Inf. Model., 2018

Fréchet ChemblNet Distance: A metric for generative models for molecules.
CoRR, 2018

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning.
Bioinform., 2018

Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields.
CoRR, 2017

GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium.
CoRR, 2017

Self-Normalizing Neural Networks.
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.
CoRR, 2015

Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map.
Bioinform., 2015

2014
Techniken des maschinellen Lernens zur Analyse von Hochdurchsatz-DNA- und RNA-Sequenzierungsdaten.
Proceedings of the Ausgezeichnete Informatikdissertationen 2014, 2014

Machine Learning Techniques for the Analysis of High-Throughput DNA and RNA Sequencing Data.
PhD thesis, 2014

2012
Enabling Large-Scale Bioinformatics Data Analysis with Cloud Computing.
Proceedings of the 10th IEEE International Symposium on Parallel and Distributed Processing with Applications, 2012


  Loading...