Brandstetter, Johannes

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

A foundation model for the Earth system

Bodnar, C., Bruinsma, W. P., Lucic, A., Stanley, M., Allen, A., Brandstetter, J., Garvan, P., Riechert, M., Weyn, J. A., Dong, H., et al. (2025) · Nature

Meteorological Phenomena and SimulationsModel Reduction and Neural Networks

Cited by 267

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

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

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