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Robust Distributed Learning

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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

Iterative Learning Control based on Stochastic Approximation

Alireza Karimi, Roland Longchamp, Mark Edward John Butcher

In this paper stochastic approximation theory is used to produce Iterative Learning Control algorithms which are less sensitive to stochastic disturbances, a typical problem for the learning process of standard ILC algorithms. Two algorithms are developed, ...

2008

Evolving Neuromodulatory Topologies for Reinforcement Learning-like Problems

Dario Floreano, Claudio Mattiussi, Peter Dürr, Andrea Soltoggio

Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in reg ...

IEEE Press2007

Spiking Neuron Networks A survey

Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of neural networks. They derive their strength and interest from an accurate modelling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs ...

IDIAP2006

Learning Multi-Modal Dictionaries: Application to Audiovisual Data

Pierre Vandergheynst, Rémi Gribonval, Gianluca Monaci, Philippe Jost

This paper presents a methodology for extracting meaningful synchronous structures from multi-modal signals. Simultaneous processing of multi-modal data can reveal information that is unavailable when handling the sources separately. However, in natural hi ...

2006

Learning and Evolution

Dario Floreano

In the last few years several researchers have resorted to artificial evolution (e.g. genetic algorithms) and learning techniques (e.g. neural networks) for studying the interaction between learning and evolution. These studies have been conducted for two ...

1999

Modeling perceptual learning: difficulties and how they can be overcome

Michael Herzog

We investigated the roles of feedback and attention in training a vernier discrimination task as an example of perceptual learning. Human learning even of simple stimuli, such as verniers, relies on more complex mechanisms than previously expected--ruling ...

Springer Verlag1998

Local Learning Algorithm For Optical Neural Networks

Demetri Psaltis

An anti-Hebbian local learning algorithm for two-layer optical neural networks is introduced. With this learning rule, the weight update for a certain connection depends only on the input and output of that connection and a global, scalar error signal. The ...

1992