17 citations · 22 across the 5 of their papers we have counts for
5 papers
Scalable Machine Learning Algorithms using Path Signatures
Csaba Tóth
The interface between stochastic analysis and machine learning is a rapidly evolving field, with path signatures - iterated integrals that provide faithful, hierarchical representa…
Random Fourier Signature Features
Csaba Toth, Harald Oberhauser, Zoltan Szabo
Tensor algebras give rise to one of the most powerful measures of similarity for sequences of arbitrary length called the signature kernel accompanied with attractive theoretical g…
Capturing Graphs with Hypo-Elliptic Diffusions
Csaba Toth, Darrick Lee, Celia Hacker +1
Convolutional layers within graph neural networks operate by aggregating information about local neighbourhood structures; one common way to encode such substructures is through ra…
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections
Csaba Toth, Patric Bonnier, Harald Oberhauser
Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies. At the heart of this is non-commutati…
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
Csaba Toth, Harald Oberhauser
We develop a Bayesian approach to learning from sequential data by using Gaussian processes (GPs) with so-called signature kernels as covariance functions. This allows to make sequ…