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Related publications (159)

Training fully connected networks with resistive memories: impact of device failures

Giorgio Cristiano, Massimo Giordano, Martina Bodini

Hardware accelerators based on two-terminal non-volatile memories (NVMs) can potentially provide competitive speed and accuracy for the training of fully connected deep neural networks (FC-DNNs), with respect to GPUs and other digital accelerators. We rece ...

2019

Quadruplet Selection Methods For Deep Embedding Learning

Erhan Gündogdu

Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification. For discrimination of the objects in fine-grained detail, we focus on deep embedding learning by ...

IEEE2019

Geometry Aware Convolutional Filters for Omnidirectional Images Representation

Pascal Frossard, Renata Khasanova

Due to their wide field of view, omnidirectional cameras are frequently used by autonomous vehicles, drones and robots for navigation and other computer vision tasks. The images captured by such cameras, are often analyzed and classified with techniques de ...

2019

A survey on policy search algorithms for learning robot controllers in a handful of trials

Sylvain Calinon

Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handf ...

2019

Learning Vision-based Flight in Drone Swarms by Imitation

Dario Floreano, Fabrizio Schiano, Julien Jean Denis Nicolas Lecoeur, Fabian Maximilian Schilling

Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This work proposes an entirely visual approach to coordinate markerless drone swarms based on imitation l ...

2019

How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

Daniel Gatica-Perez, Jean-Marc Odobez, Gülcan Can

Thanks to the digital preservation of cultural heritage materials, multimedia tools (e.g., based on automatic visual processing) considerably ease the work of scholars in the humanities and help them to perform quantitative analysis of their data. In this ...

2018

Geodesic Convolutional Shape Optimization

Pascal Fua, François Fleuret, Pierre Bruno Baqué, Edoardo Remelli

Aerodynamic shape optimization has many industrial applications. Existing methods, however, are so computationally demanding that typical engineering practices are to either simply try a limited number of hand-designed shapes or restrict oneself to shapes ...

2018

Comparing human and machine performances in transcribing 18th century handwritten Venetian script

Frédéric Kaplan, Sofia Ares Oliveira

Automatic transcription of handwritten texts has made important progress in the recent years. This increase in performance, essentially due to new architectures combining convolutional neural networks with recurrent neutral networks, opens new avenues for ...

2018

Comparing human and machine performances in transcribing 18th century handwritten Venetian script

Sofia Ares Oliveira

2018

Learning embeddings: efficient algorithms and applications

Cijo Jose

Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...

EPFL2018

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