18 papers
Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems
Jan Tauberschmidt, Sophie Fellenz, Sebastian J. Vollmer +1
We present a framework for fine-tuning flow-matching generative models to enforce physical constraints and solve inverse problems in scientific systems. Starting from a model train…
Skipping the Zeros in Diffusion Models for Sparse Data Generation
Phil Sidney Ostheimer, Mayank Nagda, Andriy Balinskyy +6
Diffusion models (DMs) excel on dense continuous data, but are not designed for sparse continuous data. They do not model exact zeros that represent the deliberate absence of a sig…
Population Risk Bounds for Kolmogorov-Arnold Networks Trained by DP-SGD with Correlated Noise
Puyu Wang, Jan Schuchardt, Nikita Kalinin +4
We establish the first population risk bounds for Kolmogorov-Arnold Networks (KANs) trained by mini-batch SGD with gradient clipping, covering non-private SGD as well as differenti…
Sparse Data Diffusion for Scientific Simulations in Biology and Physics
Phil Ostheimer, Mayank Nagda, Andriy Balinskyy +5
Sparse data is fundamental to scientific simulations in biology and physics, from single-cell gene expression to particle calorimetry, where exact zeros encode physical absence rat…
Reimagining Anomalies: What If Anomalies Were Normal?
Philipp Liznerski, Saurabh Varshneya, Ece Calikus +5
Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is pr…
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Dennis Wagner, Arjun Nair, Billy Joe Franks +24
Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection m…