Klambauer, Günter

Self-Normalizing Neural Networks

Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017) · arXiv

Neural Networks and ApplicationsMachine Learning and ELM

Cited by 2,267

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 1,081

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 584

xLSTM: Extended Long Short-Term Memory

Beck, M., Pöppel, K., Spanring, M., Auer, A., Prudnikova, O., Kopp, M., Klambauer, G., Brandstetter, J., & Hochreiter, S. (2024) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 299

xLSTM: Extended Long Short-Term Memory

Beck, M., Pöppel, K., Spanring, M., Auer, A., Prudnikova, O., Kopp, M., Klambauer, G., Brandstetter, J., & Hochreiter, S. (2024) · Advances in Neural Information Processing Systems 37

Neural Networks and ApplicationsTopic Modeling

Cited by 286

Hopfield Networks is All You Need

Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., et al. (2020) · arXiv

Neural Networks and ApplicationsNeural Networks and Reservoir Computing

Cited by 236

How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

Wittwehr, C., Aladjov, H., Ankley, G., Byrne, H. J., de Knecht, J., Heinzle, E., Klambauer, G., Landesmann, B., Luijten, M., MacKay, C., et al. (2017) · Toxicological Sciences

Animal testing and alternativesComputational Drug Discovery Methods

Cited by 161

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Akbar, R., Robert, P. A., Weber, C. R., Widrich, M., Frank, R., Pavlović, M., Scheffer, L., Chernigovskaya, M., Snapkov, I., Slabodkin, A., et al. (2022) · mAbs

Monoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approaches

Cited by 94

Visual Scene Understanding for Autonomous Driving Using Semantic Segmentation

Hofmarcher, M., Unterthiner, T., Arjona-Medina, J., Klambauer, G., Hochreiter, S., & Nessler, B. (2019) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 51

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

Roland, T., Böck, C., Tschoellitsch, T., Maletzky, A., Hochreiter, S., Meier, J., & Klambauer, G. (2022) · Journal of Medical Systems

COVID-19 diagnosis using AISARS-CoV-2 detection and testing

Cited by 42

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

Schmidinger, N., Schneckenreiter, L., Seidl, P., Schimunek, J., Hoedt, P.-J., Brandstetter, J., Mayr, A., Luukkonen, S., Hochreiter, S., & Klambauer, G. (2024) · arXiv

Machine Learning in BioinformaticsComputational Drug Discovery Methods

Cited by 8

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

Schmied, T., Adler, T., Patil, V., Beck, M., Pöppel, K., Brandstetter, J., Klambauer, G., Pascanu, R., & Hochreiter, S. (2024) · arXiv

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 2