2 citations · 2 across the 5 of their papers we have counts for
6 papers
TiRex-2: Generalizing TiRex to Multivariate Data and Streaming
Patrick Podest, Marco Pichler, Elias Bürger +7
We introduce TiRex-2, a recurrent xLSTM-based time series foundation model that generalizes the univariate TiRex to multivariate forecasting with both past and future covariates. R…
AP-OOD: Attention Pooling for Out-of-Distribution Detection
Claus Hofmann, Christian Huber, Bernhard Lehner +3
Out-of-distribution (OOD) detection, which maps high-dimensional data into a scalar OOD score, is critical for the reliable deployment of machine learning models. A key challenge i…
Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification
Andreas Auer, Daniel Klotz, Sebastinan Böck +1
Recent research on time series foundation models has primarily focused on forecasting, leaving it unclear how generalizable their learned representations are. In this study, we exa…
TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning
Andreas Auer, Patrick Podest, Daniel Klotz +3
In-context learning, the ability of large language models to perform tasks using only examples provided in the prompt, has recently been adapted for time series forecasting. This p…
Simplified priors for Object-Centric Learning
Vihang Patil, Andreas Radler, Daniel Klotz +1
Humans excel at abstracting data and constructing \emph{reusable} concepts, a capability lacking in current continual learning systems. The field of object-centric learning address…
Energy-based Hopfield Boosting for Out-of-Distribution Detection
Claus Hofmann, Simon Schmid, Bernhard Lehner +2
Out-of-distribution (OOD) detection is critical when deploying machine learning models in the real world. Outlier exposure methods, which incorporate auxiliary outlier data in the…