146 citations · 176 across the 17 of their papers we have counts for
15 papers · 1 filter
Multi-task Bias-Variance Trade-off Through Functional Constraints
Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana +1
Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is t…
Training Graph Neural Networks on Growing Stochastic Graphs
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. Based on matrix multiplications, convolutions incur in high computational…
Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs
Elvin Isufi, Fernando Gama, Alejandro Ribeiro
This paper reviews graph convolutional neural networks (GCNNs) through the lens of edge-variant graph filters. The edge-variant graph filter is a finite order, linear, and local re…
Gated Graph Convolutional Recurrent Neural Networks
Luana Ruiz, Fernando Gama, Alejandro Ribeiro
Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recur…
Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series
Weiyu Huang, Yunlong Wang, Li Zhou +3
Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of…
Functional Nonlinear Sparse Models
Luiz F. O. Chamon, Yonina C. Eldar, Alejandro Ribeiro
Signal processing is rich in inherently continuous and often nonlinear applications, such as spectral estimation, optical imaging, and super-resolution microscopy, in which sparsit…