Publications related to Penalising the biases in norm regularisation enforces sparsity

When Neurons Fail - Technical Report

Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks ...

EPFL2016

Learning to Assign Orientations to Feature Points

Pascal Fua, Vincent Lepetit, Kwang Moo Yi, Yannick Verdie

We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction ...

2016

Recurrent Convolutional Neural Networks for Scene Labeling

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

The goal of the scene labeling task is to assign a class label to each pixel in an image. To ensure a good visual coherence and a high class accu- racy, it is essential for a model to capture long range (pixel) label dependencies in images. In a feed-forwa ...

2014

Recurrent Convolutional Neural Networks for Scene Parsing

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

Scene parsing is a technique that consist on giving a label to all pixels in an image according to the class they belong to. To ensure a good visual coherence and a high class accuracy, it is essential for a scene parser to capture image long range depende ...

Idiap2013

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

Loïc Andolfatto

Abstract Tolerance analysis is an important step to validate assembly process planning scenario. Simulations are generally performed to evaluate the expected geometrical variations of the assembled product. When the simulation models take into account part ...

2013

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

Loïc Andolfatto

Tolerance analysis is an important step to validate assembly process planning scenario. Simulations are generally performed to evaluate the expected geometrical variations of the assembled product. When the simulation models take into account part complian ...

2013

Improving Object Classification using Pose Information

François Fleuret, Hugo Augusto Penedones Fernandes, Ronan Collobert, David Grangier

We propose a method that exploits pose information in order to improve object classification. A lot of research has focused in other strategies, such as engineering feature extractors, trying different classifiers and even using transfer learning. Here, we ...

Idiap2012

MERIS observations of phytoplankton blooms in a stratified eutrophic lake

Alfred Johny Wüest

The use of spaceborne medium resolution imaging spectrometers with neural network algorithms has proven a large potential for application with optically complex inland waters. We make use of this approach to investigate the bio-physical dynamics in a eutro ...

Elsevier Science Inc2012

Training Distributed Neural Networks by Consensus

Martin Hasler, Leonidas Georgopoulos

We address the problem of learning a classifier from distributed data over a number of arbitrarily connected machines without exchange of the datapoints. Our purpose is to train a neural network at each machine as if the entire dataset was locally availabl ...

2011

Network Simplification: the Gaussian N-Relay Diamond Network

Christina Fragouli, Ayfer Özgür Aydin

We consider the Gaussian

N

-relay diamond network, where a source wants to communicate to a destination through a layer of

N

-relay nodes. We investigate the following question: What fraction of the capacity can we maintain by using only

k

out of the $ ...

2010

Penalising the biases in norm regularisation enforces sparsity

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When Neurons Fail - Technical Report

Learning to Assign Orientations to Feature Points

Recurrent Convolutional Neural Networks for Scene Labeling

Recurrent Convolutional Neural Networks for Scene Parsing

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

Improving Object Classification using Pose Information

MERIS observations of phytoplankton blooms in a stratified eutrophic lake

Training Distributed Neural Networks by Consensus

Network Simplification: the Gaussian N-Relay Diamond Network

Recurrent Convolutional Neural Networks for Scene Labeling

Network Simplification: the Gaussian N-Relay Diamond Network

When Neurons Fail - Technical Report

Recurrent Convolutional Neural Networks for Scene Parsing

MERIS observations of phytoplankton blooms in a stratified eutrophic lake

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

Improving Object Classification using Pose Information

On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly

Training Distributed Neural Networks by Consensus

Learning to Assign Orientations to Feature Points