no code implementations • 5 Feb 2024 • Hans Riess, Manolis Veveakis, Michael M. Zavlanos
The path signature, having enjoyed recent success in the machine learning community, is a theoretically-driven method for engineering features from irregular paths.
no code implementations • 20 Mar 2023 • Claudio Battiloro, Zhiyang Wang, Hans Riess, Paolo Di Lorenzo, Alejandro Ribeiro
We define tangent bundle filters and tangent bundle neural networks (TNNs) based on this convolution operation, which are novel continuous architectures operating on tangent bundle signals, i. e. vector fields over the manifolds.
no code implementations • 11 Nov 2022 • Mikhail Hayhoe, Hans Riess, Victor M. Preciado, Alejandro Ribeiro
To do so, we provide a framework for bounding the stability and transferability error of GNNs across arbitrary graphs via spectral similarity.
no code implementations • 26 Oct 2022 • Claudio Battiloro, Zhiyang Wang, Hans Riess, Paolo Di Lorenzo, Alejandro Ribeiro
In this work we introduce a convolution operation over the tangent bundle of Riemannian manifolds exploiting the Connection Laplacian operator.
no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Hans Riess, Jakob Hansen, Robert Ghrist
Multiparameter persistent homology has been largely neglected as an input to machine learning algorithms.
no code implementations • 22 Oct 2020 • Alejandro Parada-Mayorga, Hans Riess, Alejandro Ribeiro, Robert Ghrist
In this paper we state the basics for a signal processing framework on quiver representations.
no code implementations • 1 Sep 2020 • Hans Riess, Yiannis Kantaros, George Pappas, Robert Ghrist
We show that these constraints along with the requirement of propagating information in the network can be captured by a Linear Temporal Logic (LTL) framework.