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03/15/2023
Joint Graph and Vertex Importance Learning
In this paper, we explore the topic of graph learning from the perspecti...
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10/31/2022
Study of Manifold Geometry using Multiscale Non-Negative Kernel Graphs
Modern machine learning systems are increasingly trained on large amount...
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10/15/2022
Motion estimation and filtered prediction for dynamic point cloud attribute compression
In point cloud compression, exploiting temporal redundancy for inter pre...
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09/18/2022
The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning
Self-supervised learning (SSL) has emerged as a desirable paradigm in co...
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05/13/2022
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
Self-supervised learning (SSL) is currently one of the premier technique...
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03/07/2022
Compression of user generated content using denoised references
Video shared over the internet is commonly referred to as user generated...
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03/02/2022
Hybrid Model-based / Data-driven Graph Transform for Image Coding
Transform coding to sparsify signal representations remains crucial in a...
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02/01/2022
Fractional Motion Estimation for Point Cloud Compression
Motivated by the success of fractional pixel motion in video coding, we ...
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10/18/2021
Channel redundancy and overlap in convolutional neural networks with channel-wise NNK graphs
Feature spaces in the deep layers of convolutional neural networks (CNNs...
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10/15/2021
NNK-Means: Dictionary Learning using Non-Negative Kernel regression
An increasing number of systems are being designed by first gathering si...
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09/17/2021
Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields
This paper presents a convex-analytic framework to learn sparse graphs f...
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08/06/2021
Estimating Active Cases of COVID-19
Having accurate and timely data on confirmed active COVID-19 cases is ch...
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07/27/2021
Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation
State-of-the-art neural network architectures continue to scale in size ...
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06/16/2021
Multi-resolution intra-predictive coding of 3D point cloud attributes
We propose an intra frame predictive strategy for compression of 3D poin...
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12/06/2020
Spatio-Temporal Graph Scattering Transform
Although spatio-temporal graph neural networks have achieved great empir...
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11/14/2020
Representing Deep Neural Networks Latent Space Geometries with Graphs
Deep Learning (DL) has attracted a lot of attention for its ability to r...
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10/25/2020
Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Learning a suitable graph is an important precursor to many graph signal...
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07/20/2020
DeepNNK: Explaining deep models and their generalization using polytope interpolation
Modern machine learning systems based on neural networks have shown grea...
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05/24/2020
CoronaSurveys: Using Surveys with Indirect Reporting to Estimate the Incidence and Evolution of Epidemics
The world is suffering from a pandemic called COVID-19, caused by the SA...
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03/09/2020
Sampling on Graphs: From Theory to Applications
The study of sampling signals on graphs, with the goal of building an an...
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03/04/2020
Region adaptive graph fourier transform for 3d point clouds
We introduce the Region Adaptive Graph Fourier Transform (RA-GFT) for co...
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01/10/2020
Time-Varying Graph Learning with Constraints on Graph Temporal Variation
We propose a novel framework for learning time-varying graphs from spati...
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12/23/2019
Reducing Storage in Large-Scale Photo Sharing Services using Recompression
The popularity of photo sharing services has increased dramatically in r...
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11/08/2019
Deep geometric knowledge distillation with graphs
In most cases deep learning architectures are trained disregarding the a...
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10/21/2019
Graph Construction from Data using Non Negative Kernel regression (NNK Graphs)
Data driven graph constructions are often used in various applications, ...
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10/01/2019
Covariance Matrix Estimation with Non Uniform and Data Dependent Missing Observations
In this paper we study covariance estimation with missing data. We consi...
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09/11/2019
Structural Robustness for Deep Learning Architectures
Deep Networks have been shown to provide state-of-the-art performance in...
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09/03/2019
Graph-based Transforms for Video Coding
In many state-of-the-art compression systems, signal transformation is a...
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05/01/2019
Introducing Graph Smoothness Loss for Training Deep Learning Architectures
We introduce a novel loss function for training deep learning architectu...
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05/24/2018
Laplacian Power Networks: Bounding Indicator Function Smoothness for Adversarial Defense
Deep Neural Networks often suffer from lack of robustness to adversarial...
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04/04/2018
Active covariance estimation by random sub-sampling of variables
We study covariance matrix estimation for the case of partially observed...
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03/07/2018
Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification
This paper introduces a novel graph signal processing framework for buil...
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05/31/2017
Learning Graphs with Monotone Topology Properties and Multiple Connected Components
Learning graphs with topology properties is a non-convex optimization pr...
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11/16/2016
Graph Learning from Data under Structural and Laplacian Constraints
Graphs are fundamental mathematical structures used in various fields to...
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10/03/2016
Dynamic Polygon Clouds: Representation and Compression for VR/AR
We introduce the polygon cloud, also known as a polygon set or soup, a...
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05/16/2014