48 citations · 52 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
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…
HANNA: Hard-constraint Neural Network for Consistent Activity Coefficient Prediction
Thomas Specht, Mayank Nagda, Sophie Fellenz +3
We present the first hard-constraint neural network for predicting activity coefficients (HANNA), a thermodynamic mixture property that is the basis for many applications in scienc…
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…
Evaluating Dynamic Topic Models
Charu James, Mayank Nagda, Nooshin Haji Ghassemi +2
There is a lack of quantitative measures to evaluate the progression of topics through time in dynamic topic models (DTMs). Filling this gap, we propose a novel evaluation measure…
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…