385 citations · 1.8k across the 36 of their papers we have counts for
7 papers · 1 filter
Matrix Low-Rank Approximation For Policy Gradient Methods
Sergio Rozada, Antonio G. Marques
Estimating a policy that maps states to actions is a central problem in reinforcement learning. Traditionally, policies are inferred from the so called value functions (VFs), but e…
Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning
Sergio Rozada, Santiago Paternain, Antonio G. Marques
Value-function (VF) approximation is a central problem in Reinforcement Learning (RL). Classical non-parametric VF estimation suffers from the curse of dimensionality. As a result,…
Tensor Graph Convolutional Networks for Multi-relational and Robust Learning
Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis
The era of "data deluge" has sparked renewed interest in graph-based learning methods and their widespread applications ranging from sociology and biology to transportation and com…
A Recurrent Graph Neural Network for Multi-Relational Data
Vassilis N. Ioannidis, Antonio G. Marques, Georgios B. Giannakis
The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering. In this paper,…
Median activation functions for graph neural networks
Luana Ruiz, Fernando Gama, Antonio G. Marques +1
Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs. By replacing regular convolu…
MIMO Graph Filters for Convolutional Neural Networks
Fernando Gama, Antonio G. Marques, Alejandro Ribeiro +1
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNNs) for a wide array of inference and reconstruction tasks. CNNs impl…
Convolutional Neural Networks Via Node-Varying Graph Filters
Fernando Gama, Geert Leus, Antonio G. Marques +1
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Sinc…