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A Versatile Mapping Approach for Technology Mapping and Graph Optimization

Publications associées (38)

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Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

ASSOC COMPUTING MACHINERY2021

node2coords: Graph Representation Learning with Wasserstein Barycenters

Pascal Frossard, Effrosyni Simou

In order to perform network analysis tasks, representations that capture the most relevant information in the graph structure are needed. However, existing methods learn representations that cannot be interpreted in a straightforward way and that are relat ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Graph Learning Under Partial Observability

Ali H. Sayed, Augusto José Rabelo Almeida Santos

Many optimization, inference, and learning tasks can be accomplished efficiently by means of decentralized processing algorithms where the network topology (i.e., the graph) plays a critical role in enabling the interactions among neighboring nodes. There ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Omnidirectional Strategies for Exploring Ancient Cities and Territories

Sarah Irene Brutton Kenderdine

By focusing on technologies of virtual reality in conjunction with theories of “place” and “presence,” this chapter outlines the importance of new approaches to the museological experience and exploration of ancient cities and cultural heritage sites. Expl ...

Oxford University Press2020

Better Patch Stitching for Parametric Surface Reconstruction

Pascal Fua, Mathieu Salzmann, Zhantao Deng, Jan Bednarík

Recently, parametric mappings have emerged as highly effective surface representations, yielding low reconstruction error. In particular, the latest works represent the target shape as an atlas of multiple mappings, which can closely encode object parts. A ...

IEEE2020

On the Experimental Transferability of Spectral Graph Convolutional Networks

Spectral Graph Convolutional Networks (GCNs) are generalisations of standard convolutional for graph-structured data using the Laplacian operator. Recent work has shown that spectral GCNs have an intrinsic transferability. This work verifies this by studyi ...

2020

On the adjustment, calibration and orientation of drone photogrammetry and laser-scanning

Emmanuel Pierre Quentin Clédat

Centimetre level precision mapping is essential for many applications such as land-use, infrastructure inspection, cultural heritage preservation, and construction site monitoring. However, the acquisition and its preparation (in particular the setting of ...

EPFL2020

Airborne and mobile LiDAR, which sensors for which application?

Jan Skaloud, Philipp Clausen, Julien Vallet

The UAV mapping industry expanded tremendously during the last five years. Thanks to miniaturization, automation and advertising, this technology may give a wrong impression that mapping of certain quality is as simple as clicking few buttons on a PC. More ...

2020

Graph-based image representation learning

Renata Khasanova

Though deep learning (DL) algorithms are very powerful for image processing tasks, they generally require a lot of data to reach their full potential. Furthermore, there is no straightforward way to impose various properties, given by the prior knowledge a ...

EPFL2019

Structurally-Informed Deconvolution Of Functional Magnetic Resonance Imaging Data

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton, Younes Farouj, Mert Inan

Neural activity occurs in the shape of spatially organized patterns: networks of brain regions activate in synchrony. Many of these functional networks also happen to be strongly structurally connected. We use this information to revisit the fundamental pr ...

IEEE2019