A high order solver for signature kernels
M Lemercier, T Lyons - arxiv preprint arxiv:2404.02926, 2024 - arxiv.org
Signature kernels are at the core of several machine learning algorithms for analysing
multivariate time series. The kernel of two bounded variation paths (such as piecewise linear …
multivariate time series. The kernel of two bounded variation paths (such as piecewise linear …
Rough Transformers: Lightweight Continuous-Time Sequence Modelling with Path Signatures
Time-series data in real-world settings typically exhibit long-range dependencies and are
observed at non-uniform intervals. In these settings, traditional sequence-based recurrent …
observed at non-uniform intervals. In these settings, traditional sequence-based recurrent …
Acquisition-Independent Deep Learning for Quantitative MRI Parameter Estimation using Neural Controlled Differential Equations
D Kuppens, S Barbieri, D Berg, P Schouten… - arxiv preprint arxiv …, 2024 - arxiv.org
Deep learning has proven to be a suitable alternative to least-squares (LSQ) fitting for
parameter estimation in various quantitative MRI (QMRI) models. However, current deep …
parameter estimation in various quantitative MRI (QMRI) models. However, current deep …
RoughPy: streaming data is rarely smooth
S Morley, T Lyons - 2024 - ora.ox.ac.uk
Rough path theory is a branch of mathematics arising out of stochastic analysis. One of the
main tools of rough path analysis is the signature, which captures the evolution of an …
main tools of rough path analysis is the signature, which captures the evolution of an …