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DiffStyleTS: Diffusion Model for Style Transfer in Time Series
Mayank Nagda, Phil Ostheimer, Justus Arweiler +13
Style transfer combines the content of one signal with the style of another. It supports applications such as data augmentation and scenario simulation, helping machine learning mo…
PIANO: Physics Informed Autoregressive Network
Mayank Nagda, Jephte Abijuru, Phil Ostheimer +2
Solving time-dependent partial differential equations (PDEs) is fundamental to modeling critical phenomena across science and engineering. Physics-Informed Neural Networks (PINNs)…
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…
Challenging Assumptions in Learning Generic Text Style Embeddings
Phil Ostheimer, Marius Kloft, Sophie Fellenz
Recent advancements in language representation learning primarily emphasize language modeling for deriving meaningful representations, often neglecting style-specific consideration…
SetPINNs: Set-based Physics-informed Neural Networks
Mayank Nagda, Phil Ostheimer, Thomas Specht +5
Physics-Informed Neural Networks (PINNs) solve partial differential equations using deep learning. However, conventional PINNs perform pointwise predictions that neglect dependenci…
A Call for Standardization and Validation of Text Style Transfer Evaluation
Phil Ostheimer, Mayank Nagda, Marius Kloft +1
Text Style Transfer (TST) evaluation is, in practice, inconsistent. Therefore, we conduct a meta-analysis on human and automated TST evaluation and experimentation that thoroughly…