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An Introduction to Bayesian Network Theory and Usage

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Spherical Convolutionnal Neural Networks: Empirical Analysis of SCNNs

Frédérick Matthieu Gusset

Convolutional neural networks (CNNs) are powerful tools in Deep Learning mainly due to their ability to exploit the translational symmetry present in images, as they are equivariant to translations. Other datasets present different types of symmetries (e.g ...

2019

Edge Ordered Turan Problems

Gábor Tardos, Abhishek Methuku

We introduce the Turan problem for edge ordered graphs. We call a simple graph edge ordered, if its edges are linearly ordered. An isomorphism between edge ordered graphs must respect the edge order. A subgraph of an edge ordered graph is itself an edge or ...

COMENIUS UNIV2019

Deep learning on graph for semantic segmentation of point cloud

Alexandre Thierry Robert Cherqui

This master thesis provides in-depth explanations of how deep learning and graph theory can be used together to perform pointwise classification in 3D point clouds obtained by combinations of geospatial images. That scene understanding problem arises in a ...

2018

A Time-Vertex Signal Processing Framework

Nathanaël Perraudin, Benjamin Ricaud, Andreas Loukas, Francesco Grassi

An emerging way to deal with high-dimensional non-euclidean data is to assume that the underlying structure can be captured by a graph. Recently, ideas have begun to emerge related to the analysis of time-varying graph signals. This work aims to elevate th ...

Institute of Electrical and Electronics Engineers2018

Compiling Questions into Balanced Quizzes about Documents

Robert West, Cristina Menghini

In the educational framework, knowledge assessment is a critical component, and quizzes (sets of questions with concise answers) are a popular tool for this purpose. This paper focuses on the generation of balanced quizzes, i.e., quizzes that relate to a g ...

ASSOC COMPUTING MACHINERY2018

Belief control strategies for interactions over weak graphs

Ali H. Sayed

In diffusion social learning over weakly-connected graphs, it has been shown that influential agents end up shaping the beliefs of non-influential agents. In this paper, we analyse this control mechanism more closely and reveal some critical properties. In ...

IEEE2017

Social Learning Over Weakly Connected Graphs

Ali H. Sayed, Bicheng Ying

In this paper, we study diffusion social learning over weakly connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarif ...

2017

Scaling a Reliable Distributed System

Alexandre David Olivier Maurer

We consider the problem of reliably connecting an arbitrarily large set of computers (nodes) with communication channels. Reliability means here the ability, for any two nodes, to remain connected (i.e., their ability to communicate) with probability at le ...

LPD EPFL2016

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets

Nathanaël Perraudin, Benjamin Ricaud, Francesco Grassi

Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolv ...

2016

Deconvolution of Gaussian Random Fields using the Graph Fourier Transform

Matthieu Martin Jean-André Simeoni

Real-life acquisition systems are fundamentally limited in their ability to reproduce point sources. For example, a point source object, a star say, observed with an optical telescope is blurred by the imperfect lenses composing the system. Mathematically, ...

2016