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no code implementations • 21 Mar 2023 • Geert Leus, Antonio G. Marques, José M. F. Moura, Antonio Ortega, David I Shuman

Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph.

no code implementations • 15 Mar 2023 • Benjamin Girault, Eduardo Pavez, Antonio Ortega

In this paper, we explore the topic of graph learning from the perspective of the Irregularity-Aware Graph Fourier Transform, with the goal of learning the graph signal space inner product to better model data.

no code implementations • 12 Mar 2023 • Ajinkya Jayawant, Antonio Ortega

In numerous graph signal processing applications, data is often missing for a variety of reasons, and predicting the missing data is essential.

no code implementations • 11 Mar 2023 • Xin Xiong, Eduardo Pavez, Antonio Ortega, Balu Adsumilli

We proposed a geometric criterion for saturation detection that works with rate-distortion optimization, and only requires a few frames from the UGC video.

no code implementations • 3 Mar 2023 • Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega

Most codec designs rely on the mean squared error (MSE) as a fidelity metric in rate-distortion optimization, which allows to choose the optimal parameters in the transform domain but may fail to reflect perceptual quality.

no code implementations • 31 Oct 2022 • Carlos Hurtado, Sarath Shekkizhar, Javier Ruiz-Hidalgo, Antonio Ortega

Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces.

no code implementations • 15 Oct 2022 • Haoran Hong, Eduardo Pavez, Antonio Ortega, Ryosuke Watanabe, Keisuke Nonaka

The scheme includes integer-precision motion estimation and an adaptive graph based in-loop filtering scheme for improved attribute prediction.

no code implementations • 18 Sep 2022 • Romain Cosentino, Sarath Shekkizhar, Mahdi Soltanolkotabi, Salman Avestimehr, Antonio Ortega

Self-supervised learning (SSL) has emerged as a desirable paradigm in computer vision due to the inability of supervised models to learn representations that can generalize in domains with limited labels.

no code implementations • 13 May 2022 • Romain Cosentino, Anirvan Sengupta, Salman Avestimehr, Mahdi Soltanolkotabi, Antonio Ortega, Ted Willke, Mariano Tepper

When used for transfer learning, the projector is discarded since empirical results show that its representation generalizes more poorly than the encoder's.

no code implementations • 7 Mar 2022 • Eduardo Pavez, Enrique Perez, Xin Xiong, Antonio Ortega, Balu Adsumilli

UGC video is uploaded by users, and then it is re-encoded to be made available at various levels of quality.

no code implementations • 6 Mar 2022 • Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou

Our approach is based on novel graph Fourier transforms (GFTs) given by the generalized eigenvectors of the variation operator.

no code implementations • 2 Mar 2022 • Saghar Bagheri, Tam Thuc Do, Gene Cheung, Antonio Ortega

Transform coding to sparsify signal representations remains crucial in an image compression pipeline.

no code implementations • 1 Feb 2022 • Haoran Hong, Eduardo Pavez, Antonio Ortega, Ryosuke Watanabe, Keisuke Nonaka

Motivated by the success of fractional pixel motion in video coding, we explore the design of motion estimation with fractional-voxel resolution for compression of color attributes of dynamic 3D point clouds.

no code implementations • 18 Oct 2021 • David Bonet, Antonio Ortega, Javier Ruiz-Hidalgo, Sarath Shekkizhar

Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to interpret.

no code implementations • 15 Oct 2021 • Sarath Shekkizhar, Antonio Ortega

An increasing number of systems are being designed by gathering significant amounts of data and then optimizing the system parameters directly using the obtained data.

no code implementations • 15 Oct 2021 • Julia Barrufet, Antonio Ortega

In this paper we consider the problem of defining transforms for signals on directed graphs, with a specific focus on defective graphs where the corresponding graph operator cannot be diagonalized.

no code implementations • 17 Sep 2021 • Tatsuya Koyakumaru, Masahiro Yukawa, Eduardo Pavez, Antonio Ortega

This paper presents a convex-analytic framework to learn sparse graphs from data.

no code implementations • 20 Aug 2021 • Feng Ji, Wee Peng Tay, Antonio Ortega

Each graph topology gives rise to a different shift operator.

1 code implementation • 27 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.

no code implementations • 21 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.

no code implementations • 16 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.

no code implementations • 22 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.

1 code implementation • 21 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.

no code implementations • 15 Feb 2021 • Yung-Hsuan Chao, Haoran Hong, Gene Cheung, Antonio Ortega

Using a conventional Bayer pattern, data captured at each pixel is a single color component (R, G or B). The sensed data then undergoes demosaicking (interpolation of RGB components per pixel) and conversion to an array of sub-aperture images (SAIs).

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.

no code implementations • 14 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.

no code implementations • 25 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.

no code implementations • 23 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.

1 code implementation • 20 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.

BIG-bench Machine Learning
Interpretability Techniques for Deep Learning
**+2**

no code implementations • 9 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.

1 code implementation • 4 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.

1 code implementation • 16 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.

no code implementations • 10 Jan 2020 • Koki Yamada, Yuichi Tanaka, Antonio Ortega

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

1 code implementation • 8 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.

3 code implementations • 21 Oct 2019 • Sarath Shekkizhar, Antonio Ortega

Data-driven neighborhood definitions and graph constructions are often used in machine learning and signal processing applications.

no code implementations • 11 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.

no code implementations • 3 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.

no code implementations • 2019 IEEE International Conference on Image Processing (ICIP) 2019 • Jiun-Yu Kao, Antonio Ortega, Dong Tian, Hassan Mansour, Anthony Vetro

Understanding human activity based on sensor information is required in many applications and has been an active research area.

Ranked #3 on Skeleton Based Action Recognition on MSR Action3D

no code implementations • 1 May 2019 • Myriam Bontonou, Carlos Lassance, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega

We introduce a novel loss function for training deep learning architectures to perform classification.

no code implementations • ICLR 2019 • 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.

no code implementations • 24 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.

no code implementations • 4 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.

1 code implementation • 7 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.

2 code implementations • 1 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

1 code implementation • 31 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.

no code implementations • 26 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.

no code implementations • 29 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.

2 code implementations • 16 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.

no code implementations • 18 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.

no code implementations • 8 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 $D(a, b)<1$.

no code implementations • 23 Mar 2015 • Akshay Gadde, Antonio Ortega

We give a probabilistic interpretation of sampling theory of graph signals.

no code implementations • 14 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.

no code implementations • 16 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.

no code implementations • 9 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.

1 code implementation • 31 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.

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