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

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

21 February 2025
Thomas Schmied
Thomas Adler
Vihang Patil
M. Beck
Korbinian Poppel
Johannes Brandstetter
G. Klambauer
Razvan Pascanu
Sepp Hochreiter
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Papers citing "A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks"

4 / 4 papers shown
Title
Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM Kernels
Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM Kernels
M. Beck
Korbinian Poppel
Phillip Lippe
Sepp Hochreiter
53
1
0
18 Mar 2025
xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
M. Beck
Korbinian Poppel
Phillip Lippe
Richard Kurle
P. Blies
G. Klambauer
Sebastian Böck
Sepp Hochreiter
LRM
38
0
0
17 Mar 2025
A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks
Faezeh Sarlakifar
Mohammadreza Mohammadzadeh Asl
Sajjad Rezvani Khaledi
Armin Salimi-Badr
AIFin
32
0
0
12 Mar 2025
Bio-xLSTM: Generative modeling, representation and in-context learning
  of biological and chemical sequences
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences
Niklas Schmidinger
Lisa Schneckenreiter
Philipp Seidl
Johannes Schimunek
Pieter-Jan Hoedt
Johannes Brandstetter
Andreas Mayr
Sohvi Luukkonen
Sepp Hochreiter
G. Klambauer
MedIm
45
4
0
06 Nov 2024
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