26 citations · 64 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
Semantic HELM: A Human-Readable Memory for Reinforcement Learning
Fabian Paischer, Thomas Adler, Markus Hofmarcher +1
Reinforcement learning agents deployed in the real world often have to cope with partially observable environments. Therefore, most agents employ memory mechanisms to approximate t…
Few-Shot Learning by Dimensionality Reduction in Gradient Space
Martin Gauch, Maximilian Beck, Thomas Adler +10
We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subsp…
History Compression via Language Models in Reinforcement Learning
Fabian Paischer, Thomas Adler, Vihang Patil +5
In a partially observable Markov decision process (POMDP), an agent typically uses a representation of the past to approximate the underlying MDP. We propose to utilize a frozen Pr…
Cross-Domain Few-Shot Learning by Representation Fusion
Thomas Adler, Johannes Brandstetter, Michael Widrich +5
In order to quickly adapt to new data, few-shot learning aims at learning from few examples, often by using already acquired knowledge. The new data often differs from the previous…
Quantum Optical Experiments Modeled by Long Short-Term Memory
Thomas Adler, Manuel Erhard, Mario Krenn +3
We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computa…