no code implementations • 19 Sep 2024 • Caio F. Deberaldini Netto, Zhiyang Wang, Luana Ruiz
We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image.
no code implementations • 8 Sep 2024 • Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
This generalization gap ensures that the GNN trained on a graph on a set of sampled points can be utilized to process other unseen graphs constructed from the same underlying manifold.
no code implementations • 25 Aug 2024 • Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
Importantly, we observe a trade-off between the generalization of GNNs and the capability to discriminate high-frequency components when facing a model mismatch.
no code implementations • 7 Jun 2024 • Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
Graph Neural Networks (GNNs) extend convolutional neural networks to operate on graphs.
no code implementations • 29 May 2023 • Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold, thus encoding geometric information.
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 • 15 Dec 2022 • Alejandro Parada-Mayorga, Zhiyang Wang, Alejandro Ribeiro
In this paper we propose a pooling approach for convolutional information processing on graphs relying on the theory of graphons and limits of dense graph sequences.
no code implementations • 20 Nov 2022 • Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
The increasing availability of geometric data has motivated the need for information processing over non-Euclidean domains modeled as manifolds.
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 • 1 Oct 2022 • Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Deep neural network architectures have been proved as a powerful technique to solve problems based on these data residing on the manifold.
no code implementations • 8 Jul 2022 • Alejandro Parada-Mayorga, Zhiyang Wang, Fernando Gama, Alejandro Ribeiro
We also conclude that in Agg-GNNs the selectivity of the mapping operators is tied to the properties of the filters only in the first layer of the CNN stage.
no code implementations • 10 Oct 2021 • Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Hence, in this paper, we analyze the stability properties of convolutional neural networks on manifolds to understand the stability of GNNs on large graphs.
no code implementations • 10 Oct 2021 • Zhiyang Wang, Luana Ruiz, Mark Eisen, Alejandro Ribeiro
We consider the problem of resource allocation in large scale wireless networks.
no code implementations • 3 Jul 2021 • Zhiyang Wang, Mark Eisen, Alejandro Ribeiro
We consider the broad class of decentralized optimal resource allocation problems in wireless networks, which can be formulated as a constrained statistical learning problems with a localized information structure.
no code implementations • 7 Jun 2021 • Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
The most important practical consequence of this analysis is to shed light on the behavior of graph filters and GNNs in large-scale graphs.
no code implementations • 14 Mar 2021 • Xiaojun Li, Alan Palazzolo, Zhiyang Wang
The finite element method (FEM) is too computationally intensive for early-stage analysis.
no code implementations • 3 Mar 2021 • Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
We further construct a manifold neural network architecture with these filters.
no code implementations • 5 Nov 2020 • Zhiyang Wang, Mark Eisen, Alejandro Ribeiro
We capture the asynchrony by modeling the activation pattern as a characteristic of each node and train a policy-based resource allocation method.
no code implementations • 23 Oct 2020 • Luana Ruiz, Zhiyang Wang, Alejandro Ribeiro
We then extend this analysis by interpreting the graphon neural network as a generating model for GNNs on deterministic and stochastic graphs instantiated from the original and perturbed graphons.
no code implementations • ICML 2020 • Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex.
no code implementations • 22 Feb 2018 • Zhiyang Wang, Ruida Zhou, Cong Shen
We consider a variant of the classic multi-armed bandit problem where the expected reward of each arm is a function of an unknown parameter.