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FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows

Publications associées (44)

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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

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

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

Structured Auto-Encoder with application to Music Genre Recognition

Michaël Defferrard

In this work, we present a technique that learns discriminative audio features for Music Information Retrieval (MIR). The novelty of the proposed technique is to design auto-encoders that make use of data structures to learn enhanced sparse data representa ...

2015

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

Hexagonal Class Representation for Fingerprinting and Facile Comparison of Petroleomic Samples

Yury Tsybin, Anton Kozhinov, Konstantin Zhurov

Because of the high complexity of petroleomic-type samples, there is a need for efficient ways of visualizing and interpreting the resulting data in mass spectrometry-based petroleomics. Over the years, several graphing approaches have become widespread, y ...

Amer Chemical Soc2013

Information Processing and Structure of Dynamical Networks

Ali Ajdari Rad

Networks are everywhere and we are confronted with many networks in our daily life. Networks such as Internet, World Wide Web, social, biological and economical networks have been subject to extensive studies in the last decade. The volume of publications ...

EPFL2011

Omnidirectional Object Duplicate Detection

Touradj Ebrahimi, Lutz Goldmann, Ivan Ivanov, Péter Vajda

In this paper, we extend a graph-based approach for omnidirectional object duplicate detection in still images. Objects are detected from several points of view with different distances. The goal of this work is to determine how many training images have t ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Automatic skill acquisition in Reinforcement Learning using connection graph stability centrality

Martin Hasler, Ali Ajdari Rad

Reinforcement Learning (RL) is an approach for training agent's behavior through trial-and-error interactions with a dynamic environment. An important problem of RL is that in large domains an enormous number of decisions are to be made. Hence, instead of ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010