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04/06/2023
Interpretable statistical representations of neural population dynamics and geometry
The dynamics of neuron populations during diverse tasks often evolve on ...
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05/31/2022
Generalised Implicit Neural Representations
We consider the problem of learning implicit neural representations (INR...
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02/17/2020
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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05/23/2019
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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05/21/2019
Revisiting hard thresholding for DNN pruning
The most common method for DNN pruning is hard thresholding of network w...
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03/20/2019
A Graph-structured Dataset for Wikipedia Research
Wikipedia is a rich and invaluable source of information. Its central pl...
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03/12/2018
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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02/21/2018
Spectrally approximating large graphs with smaller graphs
How does coarsening affect the spectrum of a general graph? We provide c...
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02/16/2018
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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12/30/2017
PAC-Bayesian Margin Bounds for Convolutional Neural Networks - Technical Report
Recently the generalisation error of deep neural networks has been analy...
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10/01/2017
Wikipedia graph mining: dynamic structure of collective memory
Wikipedia is the biggest encyclopedia ever created and the fifth most vi...
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07/14/2017
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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06/12/2017
Fast Approximate Spectral Clustering for Dynamic Networks
Spectral clustering is a widely studied problem, yet its complexity is p...
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02/19/2017
Compressive Embedding and Visualization using Graphs
Visualizing high-dimensional data has been a focus in data analysis comm...
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12/22/2016
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
This paper introduces Graph Convolutional Recurrent Network (GCRN), a de...
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11/24/2016
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a...
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11/15/2016
Multilinear Low-Rank Tensors on Graphs & Applications
We propose a new framework for the analysis of low-rank tensors which li...
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10/04/2016
Adaptive Graph-based Total Variation for Tomographic Reconstructions
Sparsity exploiting image reconstruction (SER) methods have been extensi...
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06/30/2016
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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06/22/2016
Towards stationary time-vertex signal processing
Graph-based methods for signal processing have shown promise for the ana...
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03/14/2016
Graph Based Sinogram Denoising for Tomographic Reconstructions
Limited data and low dose constraints are common problems in a variety o...
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03/10/2016
Global and Local Uncertainty Principles for Signals on Graphs
Uncertainty principles such as Heisenberg's provide limits on the time-f...
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02/05/2016
Compressive Spectral Clustering
Spectral clustering has become a popular technique due to its high perfo...
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01/11/2016
Stationary signal processing on graphs
Graphs are a central tool in machine learning and information processing...
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01/08/2016
Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation
This work formulates a novel song recommender system as a matrix complet...
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11/16/2015
Random sampling of bandlimited signals on graphs
We study the problem of sampling k-bandlimited signals on graphs. We pro...
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11/16/2015
Graph-based denoising for time-varying point clouds
Noisy 3D point clouds arise in many applications. They may be due to err...
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07/29/2015
Fast Robust PCA on Graphs
Mining useful clusters from high dimensional data has received significa...
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04/23/2015
Robust Principal Component Analysis on Graphs
Principal Component Analysis (PCA) is the most widely used tool for line...
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01/26/2015
Geodesic convolutional neural networks on Riemannian manifolds
Feature descriptors play a crucial role in a wide range of geometry anal...
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12/27/2014
Functional correspondence by matrix completion
In this paper, we consider the problem of finding dense intrinsic corres...
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08/07/2014
Matrix Completion on Graphs
The problem of finding the missing values of a matrix given a few of its...
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06/30/2014
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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02/04/2014
UNLocBoX: A MATLAB convex optimization toolbox for proximal-splitting methods
Convex optimization is an essential tool for machine learning, as many o...
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03/09/2013
Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds
Relationships between entities in datasets are often of multiple nature,...
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12/13/2012
Robust image reconstruction from multi-view measurements
We propose a novel method to accurately reconstruct a set of images repr...
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11/06/2012
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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06/11/2011
Clustering with Multi-Layer Graphs: A Spectral Perspective
Observational data usually comes with a multimodal nature, which means t...
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05/10/2010