13 citations · 15 across the 5 of their papers we have counts for
8 papers
The risk of KV cache compression
Lukas Haverbeck, Carmen Amo Alonso, Andres Felipe Posada-Moreno +2
Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bottleneck is KV cache compres…
Sailing Towards Zero-Shot State Estimation using Foundation Models Combined with a UKF
Tobin Holtmann, David Stenger, Andres Posada-Moreno +2
State estimation in control and systems engineering traditionally requires extensive manual system identification or data-collection effort. However, transformer-based foundation m…
Concept Extraction for Time Series with ECLAD-ts
Antonia Holzapfel, Andres Felipe Posada-Moreno, Sebastian Trimpe
Convolutional neural networks (CNNs) for time series classification (TSC) are being increasingly used in applications ranging from quality prediction to medical diagnosis. The blac…
On Foundation Models for Dynamical Systems from Purely Synthetic Data
Martin Ziegler, Andres Felipe Posada-Moreno, Friedrich Solowjow +1
Foundation models have demonstrated remarkable generalization, data efficiency, and robustness properties across various domains. In this paper, we explore the feasibility of found…
Scale-Preserving Automatic Concept Extraction (SPACE)
Andrés Felipe Posada-Moreno, Lukas Kreisköther, Tassilo Glander +1
Convolutional Neural Networks (CNN) have become a common choice for industrial quality control, as well as other critical applications in the Industry 4.0. When these CNNs behave i…
Scalable Concept Extraction in Industry 4.0
Andrés Felipe Posada-Moreno, Kai Müller, Florian Brillowski +3
The industry 4.0 is leveraging digital technologies and machine learning techniques to connect and optimize manufacturing processes. Central to this idea is the ability to transfor…