1,430 research outputs found
Une architecture modulaire pour l'extraction de caractéristiques en reconnaissance de phonèmes
- Dans ce papier, nous présentons une architecture appelée Modular Neural Prédictive Coding (MNPC). Elle est utilisée pour l'extraction de caractéristiques discriminantes. Cette architecture est conçue à l'aide de connaissances phonétiques. On estime les performances de cette architecture sur une tâche de reconnaissance de phonèmes extraits de la base Darpa-Timit. Une comparaison avec les méthodes de codage (LPC, MFCC et PLP) montrent une nette amélioration du taux de reconnaissance
Brain-Inspired Computational Intelligence via Predictive Coding
Artificial intelligence (AI) is rapidly becoming one of the key technologies
of this century. The majority of results in AI thus far have been achieved
using deep neural networks trained with the error backpropagation learning
algorithm. However, the ubiquitous adoption of this approach has highlighted
some important limitations such as substantial computational cost, difficulty
in quantifying uncertainty, lack of robustness, unreliability, and biological
implausibility. It is possible that addressing these limitations may require
schemes that are inspired and guided by neuroscience theories. One such theory,
called predictive coding (PC), has shown promising performance in machine
intelligence tasks, exhibiting exciting properties that make it potentially
valuable for the machine learning community: PC can model information
processing in different brain areas, can be used in cognitive control and
robotics, and has a solid mathematical grounding in variational inference,
offering a powerful inversion scheme for a specific class of continuous-state
generative models. With the hope of foregrounding research in this direction,
we survey the literature that has contributed to this perspective, highlighting
the many ways that PC might play a role in the future of machine learning and
computational intelligence at large.Comment: 37 Pages, 9 Figure
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
This survey samples from the ever-growing family of adaptive resonance theory
(ART) neural network models used to perform the three primary machine learning
modalities, namely, unsupervised, supervised and reinforcement learning. It
comprises a representative list from classic to modern ART models, thereby
painting a general picture of the architectures developed by researchers over
the past 30 years. The learning dynamics of these ART models are briefly
described, and their distinctive characteristics such as code representation,
long-term memory and corresponding geometric interpretation are discussed.
Useful engineering properties of ART (speed, configurability, explainability,
parallelization and hardware implementation) are examined along with current
challenges. Finally, a compilation of online software libraries is provided. It
is expected that this overview will be helpful to new and seasoned ART
researchers
- …