Backpropagation#
Explicit vector-Jacobian product functions for each forward operation, used by the PyTorch and JAX integrations and available for direct use in custom gradient pipelines.
Warning
Where possible, pysiglib.torch_api should be used rather than explicitly calling
backpropagation functions. Explicit backpropagation can introduce subtle errors if called
incorrectly. In addition, some pysiglib functions can only be backpropagated through
using their pysiglib.torch_api variants and do not expose explicit backpropagation functions.
- pysiglib.transform_path_backprop
- pysiglib.sig_backprop
- pysiglib.sig_combine_backprop
- pysiglib.sig_join_backprop
- pysiglib.sig_coef_backprop
- pysiglib.sig_to_log_sig_backprop
- pysiglib.logsig_to_sig_backprop
- pysiglib.log_sig_combine_backprop
- pysiglib.log_sig_join_backprop
- pysiglib.sig_kernel_backprop
- pysiglib.sig_kernel_gram_backprop
- pysiglib.branched_sig_backprop
- pysiglib.branched_sig_combine_backprop
Citation#
If you found this library useful in your research, please consider citing the paper:
@article{shmelev2025pysiglib,
title={pySigLib-Fast Signature-Based Computations on CPU and GPU},
author={Shmelev, Daniil and Salvi, Cristopher},
journal={arXiv preprint arXiv:2509.10613},
year={2025}
}