Riemannian optimisation leverages the geometry of smooth manifolds to reformulate and solve constrained optimisation problems as if they were unconstrained. By utilising techniques such as Riemannian ...

Abstract: Manifold learning techniques play a pivotal role in machine learning by revealing lower-dimensional embeddings within high-dimensional data, thereby enhancing the efficiency, ...

Abstract: Multimodal deep learning methods capture synergistic features from multiple modalities and have the potential to improve accuracy for stress detection compared to unimodal methods. However, ...

This study provides a computable, direct, and mathematically rigorous approximation to the differential geometry of class manifolds for high-dimensional data, along with non-linear projections from ...

Graph-based manifold learning and diffusion processes provide a powerful framework for extracting intrinsic geometric features from high-dimensional data. By constructing a graph where nodes represent ...

This repository contains a Python implementation of the Manifold Sculpting algorithm, as described by M. Ghashler et al. There are some tricks to the implementation which are not reported in the paper ...

This study represents a valuable step toward understanding how brain connectivity changes during reward-based motor learning. However, the evidence presented is incomplete. On one hand, the study ...