4.6k citations · 6.6k across the 56 of their papers we have counts for
11 papers · 1 filter
DeepNNK: Explaining deep models and their generalization using polytope interpolation
Sarath Shekkizhar, Antonio Ortega
Modern machine learning systems based on neural networks have shown great success in learning complex data patterns while being able to make good predictions on unseen data points.…
Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification
Hilmi E. Egilmez, Eduardo Pavez, Antonio Ortega
This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulat…
A Sampling Theory Perspective of Graph-based Semi-supervised Learning
Aamir Anis, Aly El Gamal, Salman Avestimehr +1
Graph-based methods have been quite successful in solving unsupervised and semi-supervised learning problems, as they provide a means to capture the underlying geometry of the data…
Graph-Based Manifold Frequency Analysis for Denoising
Shay Deutsch, Antonio Ortega, Gerard Medioni
We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian…
Graph Learning from Data under Structural and Laplacian Constraints
Hilmi E. Egilmez, Eduardo Pavez, Antonio Ortega
Graphs are fundamental mathematical structures used in various fields to represent data, signals and processes. In this paper, we propose a novel framework for learning/estimating…
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches
Eyal En Gad, Akshay Gadde, A. Salman Avestimehr +1
This paper studies graph-based active learning, where the goal is to reconstruct a binary signal defined on the nodes of a weighted graph, by sampling it on a small subset of the n…