Search Results for author:
Found 48 papers, 19 papers with code
Wavelets on Graphs via Spectral Graph Theory
We propose a novel method for constructing wavelet transforms of functions defined on the vertices of an arbitrary finite weighted graph.
Functional Analysis Information Theory Information Theory 42C40; 65T90
The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs.
Robust image reconstruction from multi-view measurements
The background image is common to all observed images but undergoes geometric transformations, as the scene is observed from different viewpoints.
A Compressed Sensing Framework for Magnetic Resonance Fingerprinting
Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI.
Information Theory Information Theory
UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting methods
Convex optimization is an essential tool for machine learning, as many of its problems can be formulated as minimization problems of specific objective functions.
Learning Laplacian Matrix in Smooth Graph Signal Representations
We show that the Gaussian prior leads to an efficient representation that favors the smoothness property of the graph signals.
Matrix Completion on Graphs
Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs.
Ranked #15 on
Recommendation Systems
on MovieLens 100K
(using extra training data)
Functional correspondence by matrix completion
In this paper, we consider the problem of finding dense intrinsic correspondence between manifolds using the recently introduced functional framework.
Geodesic convolutional neural networks on Riemannian manifolds
Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation.
Robust Principal Component Analysis on Graphs
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering.
Fast Robust PCA on Graphs
Clustering experiments on 7 benchmark datasets with different types of corruptions and background separation experiments on 3 video datasets show that our proposed model outperforms 10 state-of-the-art dimensionality reduction models.
Accelerated filtering on graphs using Lanczos method
Signal-processing on graphs has developed into a very active field of research during the last decade.
Numerical Analysis
Accelerated Spectral Clustering Using Graph Filtering Of Random Signals
We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm.
Social and Information Networks Numerical Analysis
Graph-based denoising for time-varying point clouds
Noisy 3D point clouds arise in many applications.
Random sampling of bandlimited signals on graphs
On the contrary, the second strategy is adaptive but yields optimal results.
Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation
This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs.
Stationary signal processing on graphs
Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets.
Compressive PCA for Low-Rank Matrices on Graphs
We introduce a novel framework for an approxi- mate recovery of data matrices which are low-rank on graphs, from sampled measurements.
Compressive Spectral Clustering
Spectral clustering has become a popular technique due to its high performance in many contexts.
Global and Local Uncertainty Principles for Signals on Graphs
Accordingly, we suggest a new way to incorporate a notion of locality, and develop local uncertainty principles that bound the concentration of the analysis coefficients of each atom of a localized graph spectral filter frame in terms of quantities that depend on the local structure of the graph around the center vertex of the given atom.
Graph Based Sinogram Denoising for Tomographic Reconstructions
This makes the sinogram an ideal candidate for graph based denoising since it generally has a piecewise smooth structure.
Source Localization on Graphs via l1 Recovery and Spectral Graph Theory
We cast the problem of source localization on graphs as the simultaneous problem of sparse recovery and diffusion kernel learning.
Low-Rank Matrices on Graphs: Generalized Recovery & Applications
Many real world datasets subsume a linear or non-linear low-rank structure in a very low-dimensional space.
Towards stationary time-vertex signal processing
Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks.
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.
Ranked #4 on
Skeleton Based Action Recognition
on SBU
Adaptive Graph-based Total Variation for Tomographic Reconstructions
Sparsity exploiting image reconstruction (SER) methods have been extensively used with Total Variation (TV) regularization for tomographic reconstructions.
Multilinear Low-Rank Tensors on Graphs & Applications
We propose a new framework for the analysis of low-rank tensors which lies at the intersection of spectral graph theory and signal processing.
Geometric deep learning: going beyond Euclidean data
In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques.
FMA: A Dataset For Music Analysis
We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections.
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data.
Compressive Embedding and Visualization using Graphs
Visualizing high-dimensional data has been a focus in data analysis communities for decades, which has led to the design of many algorithms, some of which are now considered references (such as t-SNE for example).
Fast Approximate Spectral Clustering for Dynamic Networks
Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size.
Wikipedia graph mining: dynamic structure of collective memory
The model exploits collective effect of the dynamics to discover events.
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains.
Signal Processing
PAC-Bayesian Margin Bounds for Convolutional Neural Networks
Recently the generalization error of deep neural networks has been analyzed through the PAC-Bayesian framework, for the case of fully connected layers.
Cheap DNN Pruning with Performance Guarantees
Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers often with little or no drop in classification accuracy.
Joint Estimation of Room Geometry and Modes with Compressed Sensing
Acoustical behavior of a room for a given position of microphone and sound source is usually described using the room impulse response.
Spectrally approximating large graphs with smaller graphs
How does coarsening affect the spectrum of a general graph?
FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees
Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers, often with little or no drop in classification accuracy.
Anomaly detection in the dynamics of web and social networks
We define an anomaly as a localized increase in temporal activity in a cluster of nodes.
A Graph-structured Dataset for Wikipedia Research
It makes the parsing and extraction of relevant information cumbersome.
Revisiting hard thresholding for DNN pruning
Recently developed smart pruning algorithms use the DNN response over the training set for a variety of cost functions to determine redundant network weights, leading to less accuracy degradation and possibly less retraining time.
The role of invariance in spectral complexity-based generalization bounds
Deep convolutional neural networks (CNNs) have been shown to be able to fit a random labeling over data while still being able to generalize well for normal labels.
SpectroBank: A filter-bank convolutional layer for CNN-based audio applications
This layer aims at being the input layer of convolutional neural networks for audio applications.
What is Trending on Wikipedia? Capturing Trends and Language Biases Across Wikipedia Editions
In this work, we propose an automatic evaluation and comparison of the browsing behavior of Wikipedia readers that can be applied to any language editions of Wikipedia.
Social and Information Networks Computers and Society
Generalised Implicit Neural Representations
We consider the problem of learning implicit neural representations (INRs) for signals on non-Euclidean domains.
Interpretable statistical representations of neural population dynamics and geometry
The dynamics of neuron populations during many behavioural tasks evolve on low-dimensional manifolds.
Implicit Gaussian process representation of vector fields over arbitrary latent manifolds
Gaussian processes (GPs) are popular nonparametric statistical models for learning unknown functions and quantifying the spatiotemporal uncertainty in data.
