30 citations · 59 across the 6 of their papers we have counts for
6 papers
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning
Christian Steinparz, Thomas Schmied, Fabian Paischer +5
In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a…
Hopular: Modern Hopfield Networks for Tabular Data
Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling +1
While Deep Learning excels in structured data as encountered in vision and natural language processing, it failed to meet its expectations on tabular data. For tabular data, Suppor…
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…
A Dataset Perspective on Offline Reinforcement Learning
Kajetan Schweighofer, Andreas Radler, Marius-Constantin Dinu +5
The application of Reinforcement Learning (RL) in real world environments can be expensive or risky due to sub-optimal policies during training. In Offline RL, this problem is avoi…
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner +8
CLIP yielded impressive results on zero-shot transfer learning tasks and is considered as a foundation model like BERT or GPT3. CLIP vision models that have a rich representation a…
Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP
Peter Knaus, Angela Bitto-Nemling, Annalisa Cadonna +1
Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting in TV…