Beck, Maximilian

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

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

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

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

CWM: An Open-Weights LLM for Research on Code Generation with World Models

FAIR CodeGen team, Copet, J., Carbonneaux, Q., Cohen, G., Gehring, J., Kahn, J., Kossen, J., Kreuk, F., McMilin, E., Meyer, M., et al. (2025) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 0