92 citations · 114 across the 9 of their papers we have counts for
4 papers · 1 filter
FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware
Korbinian Pöppel, Maximilian Beck, Sepp Hochreiter
While Transformers and other sequence-parallelizable neural network architectures seem like the current state of the art in sequence modeling, they specifically lack state-tracking…
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks
Thomas Schmied, Thomas Adler, Vihang Patil +6
In recent years, there has been a trend in the field of Reinforcement Learning (RL) towards large action models trained offline on large-scale datasets via sequence modeling. Exist…
Vision-LSTM: xLSTM as Generic Vision Backbone
Benedikt Alkin, Maximilian Beck, Korbinian Pöppel +2
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has…
xLSTM: Extended Long Short-Term Memory
Maximilian Beck, Korbinian Pöppel, Markus Spanring +6
In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and co…