An implementation of the relaxed segmented model with TSP-based warping functions. The methodology is described and analysed in detail in the paper:
Erik Scharwächter, Jonathan Lennartz and Emmanuel Müller: Differentiable Segmentation of Sequences. In: Proceedings of the International Conference on Learning Representations (ICLR), 2021. [OpenReview] [arXiv]
We provide a simple Python module nwarp.py that contains PyTorch modules for all required components. The Jupyter notebooks demonstrate how to use the components for a large variety of tasks: Poisson regression on COVID-19 data (eval-covid19.ipynb), change point detection (eval-gaussiancp.ipynb), classification under concept drift (eval-conceptdrift.ipynb), and phoneme segmentation (eval-timit.ipynb).
- Corresponding author: Erik Scharwächter
- Please cite our paper if you use or modify our code for your own work. Here's a
bibtexsnippet:
@inproceedings{Scharwachter2021,
author = {Scharw{\"{a}}chter, Erik and Lennartz, Jonathan and M{\"{u}}ller, Emmanuel},
booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)},
title = {{Differentiable Segmentation of Sequences}},
year = {2021}
}
The nwarp module itself requires torch and libcpab (for the CPA-based warping functions). The Jupyter notebooks have additional dependencies that can be checked in the respective files.
The source codes are released under the MIT license. The data in RKI_COVID19.csv are published with the title "Fallzahlen in Deutschland" by Robert Koch Institute (RKI) under the Data licence Germany – attribution – Version 2.0 (dl-de/by-2-0)