3 papers
cs.LG2026
Velocity Scheduled Flow Matching
Vitalii Bondar
Flow matching trains a neural network to regress the conditional velocity along a linear interpolant between noise and data, and the number of network evaluations~(NFE) sets the co…
eess.SP2025
FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generator
Vitalii Bondar, Serhii Semenov, Vira Babenko +1
Synthetic electrocardiogram generation serves medical AI applications requiring privacy-preserving data sharing and training dataset augmentation. Current diffusion-based methods a…
cs.LG2025
Deep generative models as the probability transformation functions
Vitalii Bondar, Vira Babenko, Roman Trembovetskyi +2
This paper introduces a unified theoretical perspective that views deep generative models as probability transformation functions. Despite the apparent differences in architecture…