Background With the development of today's technology, and as humans tend to naturally
use hand gestures in their communication process to clarify their intentions, hand gesture …

We present an efficient approach for leveraging the knowledge from multiple modalities in
training unimodal 3D convolutional neural networks (3D-CNNs) for the task of dynamic hand …

Hand gestures are a natural way for human-robot interaction. Vision based dynamic hand
gesture recognition has become a hot research topic due to its various applications. This …

Signatory is a library for calculating and performing functionality related to the signature and
logsignature transforms. The focus is on machine learning, and as such includes features …

Mobile technologies offer new opportunities for prospective, high resolution monitoring of
long-term health conditions. The opportunities seem of particular promise in psychiatry …

The signature is an infinite graded sequence of statistics known to characterise a stream of
data up to a negligible equivalence class. It is a transform which has previously been treated …

Mathematical models, calibrated to data, have become ubiquitous to make key decision
processes in modern quantitative finance. In this work, we propose a novel framework for …

The'signature method'refers to a collection of feature extraction techniques for multivariate
time series, derived from the theory of controlled differential equations. There is a great deal …

Feature-based machine learning models for capacity and internal resistance (IR) curve
prediction have been researched extensively in literature due to their high accuracy and …

This paper contributes to the challenge of learning a function on streamed multimodal data
through evaluation. The core of the result of our paper is the combination of two quite …