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On the Experimental Transferability of Spectral Graph Convolutional Networks

Publications associées (49)

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Graph-Based Classification of Omnidirectional Images

Pascal Frossard, Renata Khasanova

Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view. Their images are often processed with classical methods, which might unfortunately lead to non-optimal solutions as these methods ar ...

2017

Conditional expanding bounds for two-variable functions over finite valuation rings

Van Thang Pham

In this paper, we use methods from spectral graph theory to obtain some results on the sum-product problem over finite valuation rings R of order q(r) which generalize recent results given by Hegyvari and Hennecart (2013). More precisely, we prove that, fo ...

Academic Press Ltd- Elsevier Science Ltd2017

Graph Signal Processing

Ntorina Thanou

Over the past few decades we have been experiencing an explosion of information generated by large networks of sensors and other data sources. Much of this data is intrinsically structured, such as traffic evolution in a transportation network, temperature ...

EPFL2016

From data to structures

Vasilis Kalofolias

Weighted undirected graphs are a simple, yet powerful way to encode structure in data. A first question we need to address regarding such graphs is how to use them effectively to enhance machine learning problems. A second but more important question is ho ...

EPFL2016

Learning Laplacian Matrix in Smooth Graph Signal Representations

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not alwa ...

Institute of Electrical and Electronics Engineers2016

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Pierre Vandergheynst, Xavier Bresson, Michaël Defferrard

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 wor ...

2016

MATHICSE Technical Report : Accelerated filtering on graphs using Lanczos method

Pierre Vandergheynst, Daniel Kressner, Nathanaël Perraudin, Ana Susnjara

Sinal-processing on graphs has developed into a very active field of research during the last decade. In particular, the number of applications using frames con-structed from graphs, like wavelets on graphs, has substantially increased. To attain scalabili ...

MATHICSE2015

Laplacian Matrix Learning for Smooth Graph Signal Representation

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the emerging field of signal processing on graphs. In this paper, we address the problem of learning graph Laplacians, which is similar to learning graph topologies, such that the input data fo ...

2015

Scale-up Graph Processing in the Cloud: Challenges and Solutions

Willy Zwaenepoel, Amitabha Roy, Jasmina Malicevic

Processing large graphs is an important part of the big-data problem. Recently a number of scale-up systems such as X-Stream, Graphchi and Turbograph have been proposed for processing large graphs using secondary storage on a single machine. The design and ...

Graph-based Image Inpainting

Michaël Defferrard

The project goal was to explore the applications of spectral graph theory to address the inpainting problem of large missing chunks. We used a non-local patch graph representation of the image and proposed a structure detector which leverages the graph rep ...

2014