Hochreiter, Sepp

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation

Neural Networks and ApplicationsHydrological Forecasting Using AI

Cited by 102,318

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

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv

Generative Adversarial Networks and Image SynthesisStochastic Gradient Optimization Techniques

Cited by 8,365

Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 4,513

The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

Hochreiter, S. (1998) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

Neural Networks and ApplicationsHydrological Forecasting Using AI

Cited by 2,939

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

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Kratzert, F., Klotz, D., Hochreiter, S., & Nearing, G. S. (2021) · Hydrology and Earth System Sciences

Hydrological Forecasting Using AIHydrology and Watershed Management Studies

Cited by 213

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

Complex Networks Govern Coiled-Coil Oligomerization – Predicting and Profiling by Means of a Machine Learning Approach

Mahrenholz, C. C., Abfalter, I. G., Bodenhofer, U., Volkmer, R., & Hochreiter, S. (2011) · Molecular & Cellular Proteomics

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 47

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

Vision-LSTM: xLSTM as Generic Vision Backbone

Alkin, B., Beck, M., Pöppel, K., Hochreiter, S., & Brandstetter, J. (2024) · arXiv

Advanced Neural Network ApplicationsMultimodal Machine Learning Applications

Cited by 39

Machine Learning in Drug Discovery

Hochreiter, S., Klambauer, G., & Rarey, M. (2018) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 36

xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference

Beck, M., Pöppel, K., Lippe, P., Kurle, R., Blies, P. M., Klambauer, G., Böck, S., & Hochreiter, S. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

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

Large Language Models Can Self-Improve At Web Agent Tasks

Patel, A., Hofmarcher, M., Leoveanu-Condrei, C., Dinu, M.-C., Callison-Burch, C., & Hochreiter, S. (2024) · arXiv

Information Retrieval and Search BehaviorWeb Data Mining and Analysis

Cited by 1