
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 b...
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Some limitations of norm based generalization bounds in deep neural networks
Deep convolutional neural networks have been shown to be able to fit a l...
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Revisiting hard thresholding for DNN pruning
The most common method for DNN pruning is hard thresholding of network w...
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A Graphstructured Dataset for Wikipedia Research
Wikipedia is a rich and invaluable source of information. Its central pl...
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FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees
Recent DNN pruning algorithms have succeeded in reducing the number of p...
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Spectrally approximating large graphs with smaller graphs
How does coarsening affect the spectrum of a general graph? We provide c...
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Joint Estimation of Room Geometry and Modes with Compressed Sensing
Acoustical behavior of a room for a given position of microphone and sou...
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PACBayesian Margin Bounds for Convolutional Neural Networks  Technical Report
Recently the generalisation error of deep neural networks has been analy...
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Wikipedia graph mining: dynamic structure of collective memory
Wikipedia is the biggest encyclopedia ever created and the fifth most vi...
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Localization of Sound Sources in a Room with One Microphone
Estimation of the location of sound sources is usually done using microp...
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Fast Approximate Spectral Clustering for Dynamic Networks
Spectral clustering is a widely studied problem, yet its complexity is p...
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Compressive Embedding and Visualization using Graphs
Visualizing highdimensional data has been a focus in data analysis comm...
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Structured Sequence Modeling with Graph Convolutional Recurrent Networks
This paper introduces Graph Convolutional Recurrent Network (GCRN), a de...
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Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a...
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Multilinear LowRank Tensors on Graphs & Applications
We propose a new framework for the analysis of lowrank tensors which li...
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Adaptive Graphbased Total Variation for Tomographic Reconstructions
Sparsity exploiting image reconstruction (SER) methods have been extensi...
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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
In this work, we are interested in generalizing convolutional neural net...
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Towards stationary timevertex signal processing
Graphbased methods for signal processing have shown promise for the ana...
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Graph Based Sinogram Denoising for Tomographic Reconstructions
Limited data and low dose constraints are common problems in a variety o...
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Global and Local Uncertainty Principles for Signals on Graphs
Uncertainty principles such as Heisenberg's provide limits on the timef...
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Compressive Spectral Clustering
Spectral clustering has become a popular technique due to its high perfo...
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Stationary signal processing on graphs
Graphs are a central tool in machine learning and information processing...
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Song Recommendation with NonNegative Matrix Factorization and Graph Total Variation
This work formulates a novel song recommender system as a matrix complet...
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Random sampling of bandlimited signals on graphs
We study the problem of sampling kbandlimited signals on graphs. We pro...
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Graphbased denoising for timevarying point clouds
Noisy 3D point clouds arise in many applications. They may be due to err...
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Fast Robust PCA on Graphs
Mining useful clusters from high dimensional data has received significa...
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Robust Principal Component Analysis on Graphs
Principal Component Analysis (PCA) is the most widely used tool for line...
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Geodesic convolutional neural networks on Riemannian manifolds
Feature descriptors play a crucial role in a wide range of geometry anal...
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Functional correspondence by matrix completion
In this paper, we consider the problem of finding dense intrinsic corres...
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Matrix Completion on Graphs
The problem of finding the missing values of a matrix given a few of its...
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Learning Laplacian Matrix in Smooth Graph Signal Representations
The construction of a meaningful graph plays a crucial role in the succe...
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UNLocBoX: A MATLAB convex optimization toolbox for proximalsplitting methods
Convex optimization is an essential tool for machine learning, as many o...
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Clustering on MultiLayer Graphs via Subspace Analysis on Grassmann Manifolds
Relationships between entities in datasets are often of multiple nature,...
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Robust image reconstruction from multiview measurements
We propose a novel method to accurately reconstruct a set of images repr...
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From Bits to Images: Inversion of Local Binary Descriptors
Local Binary Descriptors are becoming more and more popular for image ma...
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Clustering with MultiLayer Graphs: A Spectral Perspective
Observational data usually comes with a multimodal nature, which means t...
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Classification via Incoherent Subspaces
This article presents a new classification framework that can extract in...
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Pierre Vandergheynst
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Professor of Electrical Engineering, Swiss Federal Institute of Technology (EPFL), Vice President at Swiss Federal Institute of Technology Lausanne