Search Results for author: Antonio Ortega

Found 37 papers, 10 papers with code

Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation

no code implementations27 Jul 2021 David Bonet, Antonio Ortega, Javier Ruiz-Hidalgo, Sarath Shekkizhar

Motivated by our observations, we use CW-DeepNNK to propose a novel early stopping criterion that (i) does not require a validation set, (ii) is based on a task performance metric, and (iii) allows stopping to be reached at different points for each channel.

Cylindrical coordinates for LiDAR point cloud compression

no code implementations21 Jun 2021 Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega

We present an efficient voxelization method to encode the geometry and attributes of 3D point clouds obtained from autonomous vehicles.

Autonomous Vehicles

Multi-resolution intra-predictive coding of 3D point cloud attributes

no code implementations16 Jun 2021 Eduardo Pavez, Andre L. Souto, Ricardo L. De Queiroz, Antonio Ortega

We propose an intra frame predictive strategy for compression of 3D point cloud attributes.

DCT and DST Filtering with Sparse Graph Operators

no code implementations22 Mar 2021 Keng-Shih Lu, Antonio Ortega, Debargha Mukherjee, Yue Chen

These sparse operators can be viewed as graph filters operating in the DCT domain, which allows us to approximate any DCT graph filter by a MPGF, leading to a design with more degrees of freedom than the conventional PGF approach.

Practical graph signal sampling with log-linear size scaling

no code implementations21 Feb 2021 Ajinkya Jayawant, Antonio Ortega

Graph signal sampling is the problem of selecting a subset of representative graph vertices whose values can be used to interpolate missing values on the remaining graph vertices.

Spatio-Temporal Graph Scattering Transform

no code implementations ICLR 2021 Chao Pan, Siheng Chen, Antonio Ortega

Although spatio-temporal graph neural networks have achieved great empirical success in handling multiple correlated time series, they may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data.

Time Series

Representing Deep Neural Networks Latent Space Geometries with Graphs

no code implementations14 Nov 2020 Carlos Lassance, Vincent Gripon, Antonio Ortega

However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought.

Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection

no code implementations25 Oct 2020 Saghar Bagheri, Gene Cheung, Antonio Ortega, Fen Wang

Learning a suitable graph is an important precursor to many graph signal processing (GSP) pipelines, such as graph spectral signal compression and denoising.

Denoising Graph Learning

Spectral folding and two-channel filter-banks on arbitrary graphs

no code implementations23 Oct 2020 Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou

A major limitation is that this framework can only be applied to the normalized Laplacian of bipartite graphs.

DeepNNK: Explaining deep models and their generalization using polytope interpolation

1 code implementation20 Jul 2020 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.

Interpretability Techniques for Deep Learning Interpretable Machine Learning +1

Sampling Signals on Graphs: From Theory to Applications

no code implementations9 Mar 2020 Yuichi Tanaka, Yonina C. Eldar, Antonio Ortega, Gene Cheung

In this article, we review current progress on sampling over graphs focusing on theory and potential applications.

Region adaptive graph fourier transform for 3d point clouds

1 code implementation4 Mar 2020 Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou

Since clusters may have a different numbers of points, each block transform must incorporate the relative importance of each coefficient.

Efficient graph construction for image representation

1 code implementation16 Feb 2020 Sarath Shekkizhar, Antonio Ortega

Graphs are useful to interpret widely used image processing methods, e. g., bilateral filtering, or to develop new ones, e. g., kernel based techniques.

Image and Video Processing Signal Processing

Time-Varying Graph Learning with Constraints on Graph Temporal Variation

no code implementations10 Jan 2020 Koki Yamada, Yuichi Tanaka, Antonio Ortega

We propose a novel framework for learning time-varying graphs from spatiotemporal measurements.

Graph Learning

Deep geometric knowledge distillation with graphs

1 code implementation8 Nov 2019 Carlos Lassance, Myriam Bontonou, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega

Specifically we introduce a graph-based RKD method, in which graphs are used to capture the geometry of latent spaces.

Knowledge Distillation

Graph Construction from Data using Non Negative Kernel regression (NNK Graphs)

3 code implementations21 Oct 2019 Sarath Shekkizhar, Antonio Ortega

Data driven graph constructions are often used in various applications, including several machine learning tasks, where the goal is to make predictions and discover patterns.

graph construction Graph Learning

Structural Robustness for Deep Learning Architectures

no code implementations11 Sep 2019 Carlos Lassance, Vincent Gripon, Jian Tang, Antonio Ortega

Deep Networks have been shown to provide state-of-the-art performance in many machine learning challenges.

Graph-based Transforms for Video Coding

no code implementations3 Sep 2019 Hilmi E. Egilmez, Yung-Hsuan Chao, Antonio Ortega

In many state-of-the-art compression systems, signal transformation is an integral part of the encoding and decoding process, where transforms provide compact representations for the signals of interest.

Video Compression

Laplacian Networks: Bounding Indicator Function Smoothness for Neural Network Robustness

no code implementations24 May 2018 Carlos Eduardo Rosar Kos Lassance, Vincent Gripon, Antonio Ortega

For the past few years, Deep Neural Network (DNN) robustness has become a question of paramount importance.

Active covariance estimation by random sub-sampling of variables

no code implementations4 Apr 2018 Eduardo Pavez, Antonio Ortega

We apply our analysis in an active learning framework, where the expected number of observed variables is small compared to the dimension of the vector of interest, and propose a design of optimal sub-sampling probabilities and an active covariance matrix estimation algorithm.

Active Learning

Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification

1 code implementation7 Mar 2018 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.

Graph Learning

Graph Signal Processing: Overview, Challenges and Applications

1 code implementation1 Dec 2017 Antonio Ortega, Pascal Frossard, Jelena Kovačević, José M. F. Moura, Pierre Vandergheynst

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains.

Signal Processing

Learning Graphs with Monotone Topology Properties and Multiple Connected Components

1 code implementation31 May 2017 Eduardo Pavez, Hilmi E. Egilmez, Antonio Ortega

Then, a graph weight estimation (GWE) step is performed by solving a generalized graph Laplacian estimation problem, where edges are constrained by the topology found in the GTI step.

A Sampling Theory Perspective of Graph-based Semi-supervised Learning

no code implementations26 May 2017 Aamir Anis, Aly El Gamal, Salman Avestimehr, Antonio Ortega

In this work, we reinforce this connection by viewing the problem from a graph sampling theoretic perspective, where class indicator functions are treated as bandlimited graph signals (in the eigenvector basis of the graph Laplacian) and label prediction as a bandlimited reconstruction problem.

Graph Sampling

Graph-Based Manifold Frequency Analysis for Denoising

no code implementations29 Nov 2016 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.

Denoising

Graph Learning from Data under Structural and Laplacian Constraints

2 code implementations16 Nov 2016 Hilmi E. Egilmez, Eduardo Pavez, Antonio Ortega

For the proposed graph learning problems, specialized algorithms are developed by incorporating the graph Laplacian and structural constraints.

Graph Learning

Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches

no code implementations18 May 2016 Eyal En Gad, Akshay Gadde, A. Salman Avestimehr, Antonio Ortega

A new sampling algorithm is proposed, which sequentially selects the graph nodes to be sampled, based on an aggressive search for the boundary of the signal over the graph.

Active Learning

Active Learning for Community Detection in Stochastic Block Models

no code implementations8 May 2016 Akshay Gadde, Eyal En Gad, Salman Avestimehr, Antonio Ortega

Our main result is to show that, under certain conditions, sampling the labels of a vanishingly small fraction of nodes (a number sub-linear in $n$) is sufficient for exact community detection even when $D(a, b)<1$.

Active Learning Community Detection +1

Asymptotic Justification of Bandlimited Interpolation of Graph signals for Semi-Supervised Learning

no code implementations14 Feb 2015 Aamir Anis, Aly El Gamal, A. Salman Avestimehr, Antonio Ortega

Graph-based methods play an important role in unsupervised and semi-supervised learning tasks by taking into account the underlying geometry of the data set.

Active Semi-Supervised Learning Using Sampling Theory for Graph Signals

no code implementations16 May 2014 Akshay Gadde, Aamir Anis, Antonio Ortega

The sampling theory for graph signals aims to extend the traditional Nyquist-Shannon sampling theory by allowing us to identify the class of graph signals that can be reconstructed from their values on a subset of vertices.

Active Learning

Localized Iterative Methods for Interpolation in Graph Structured Data

no code implementations9 Oct 2013 Sunil K. Narang, Akshay Gadde, Eduard Sanou, Antonio Ortega

In this paper, we present two localized graph filtering based methods for interpolating graph signals defined on the vertices of arbitrary graphs from only a partial set of samples.

The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains

1 code implementation31 Oct 2012 David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, Pierre Vandergheynst

In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs.

Cannot find the paper you are looking for? You can Submit a new open access paper.