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Probabilistic methods for neural combinatorial optimization

Publications associées (145)

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Human Gait Analysis and Classification Based on Neural Networks and Fuzzy Logic

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Human gait analysis and classification is the process of identifying individuals by their walking manners. Computerized gait analysis using neural networks and fuzzy logic has become an integral part of the treatment decision-making process. Authors propos ...

2009

Neuroevolution: from architectures to learning

Dario Floreano, Claudio Mattiussi, Peter Dürr

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

2008

Application of neuro-fuzzy approach in prediction of runup in swash zone

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A prominent parameter in dealing with swash and morphological evolution is the runup length or height, defined as the limit of landward sea. Therefore, it is necessary to predict the runup height in this area. In this paper, the abilities of a new Adaptive ...

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Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods

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We propose a highly efficient framework for penalized likelihood kernel methods applied to multi-class models with a large, structured set of classes. As opposed to many previous approaches which try to decompose the fitting problem into many smaller ones, ...

Massachusetts Institute of Technology Press2008

Reservoir Optimization in Recurrent Neural Networks using Kronecker Kernels

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In this paper, using the mathematical properties of self-kronecker-production of small size random matrices, a simple but effective method is presented to optimize the reservoir of an echo state network given a certain task. The experimental results invest ...

IEEE2008

On pooling networks and fluctuation in suboptimal detection framework

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Motivated by biological neural networks and distributed sensing networks, we study how pooling networks – or quantizers – with random thresholds can be used in detection tasks. We provide a brief overview of the use of deterministic quantizers in detection ...

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Improving Face Authetication Using Virtual Samples

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In this paper, we present a simple yet effective way to improve a face verification system by generating multiple virtual samples from the unique image corresponding to an access request. These images are generated using simple geometric transformations. T ...

2003

Improving Face Authetication Using Virtual Samples

Sébastien Marcel, Samy Bengio, Norman Hoon Thian Poh

IDIAP2002

Mixture models for unsupervised and supervised learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

EPFL2000

Mixture Models for Unsupervised and Supervised Learning

In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of all this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...

IDIAP2000