4.5k citations · 8.5k across the 90 of their papers we have counts for
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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…
Addressing Pitfalls in the Evaluation of Uncertainty Estimation Methods for Natural Language Generation
Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger +1
Hallucinations are a common issue that undermine the reliability of large language models (LLMs). Recent studies have identified a specific subset of hallucinations, known as confa…
xLSTM Scaling Laws: Competitive Performance with Linear Time-Complexity
Maximilian Beck, Kajetan Schweighofer, Sebastian Böck +2
Scaling laws play a central role in the success of Large Language Models (LLMs), enabling the prediction of model performance relative to compute budgets prior to training. While T…
Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned
Kajetan Schweighofer, Barbara Brune, Lukas Gruber +11
There is an increasing adoption of artificial intelligence in safety-critical applications, yet practical schemes for certifying that AI systems are safe, lawful and socially accep…
pLSTM: parallelizable Linear Source Transition Mark networks
Korbinian Pöppel, Richard Freinschlag, Thomas Schmied +2
Modern recurrent architectures, such as xLSTM and Mamba, have recently challenged the Transformer in language modeling. However, their structure constrains their applicability to s…
Rethinking Losses for Diffusion Bridge Samplers
Sebastian Sanokowski, Lukas Gruber, Christoph Bartmann +2
Diffusion bridges are a promising class of deep-learning methods for sampling from unnormalized distributions. Recent works show that the Log Variance (LV) loss consistently outper…
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…
xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference
Maximilian Beck, Korbinian Pöppel, Phillip Lippe +5
Recent breakthroughs in solving reasoning, math and coding problems with Large Language Models (LLMs) have been enabled by investing substantial computation budgets at inference ti…
Tiled Flash Linear Attention: More Efficient Linear RNN and xLSTM Kernels
Maximilian Beck, Korbinian Pöppel, Phillip Lippe +1
Linear RNNs with gating recently demonstrated competitive performance compared to Transformers in language modeling. Although their linear compute scaling in sequence length offers…
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
Sebastian Sanokowski, Wilhelm Berghammer, Martin Ennemoser +3
Learning to sample from complex unnormalized distributions over discrete domains emerged as a promising research direction with applications in statistical physics, variational inf…