1 citations · 4 across the 9 of their papers we have counts for
9 papers
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…
BBPOS: BERT-based Part-of-Speech Tagging for Uzbek
Latofat Bobojonova, Arofat Akhundjanova, Phil Ostheimer +1
This paper advances NLP research for the low-resource Uzbek language by evaluating two previously untested monolingual Uzbek BERT models on the part-of-speech (POS) tagging task an…
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…
Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors
Mayank Nagda, Phil Ostheimer, Sophie Fellenz
Topic models are a popular approach for extracting semantic information from large document collections. However, recent studies suggest that the topics generated by these models o…
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…
Text Style Transfer Evaluation Using Large Language Models
Phil Ostheimer, Mayank Nagda, Marius Kloft +1
Evaluating Text Style Transfer (TST) is a complex task due to its multifaceted nature. The quality of the generated text is measured based on challenging factors, such as style tra…
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…