Learning Multidimensional Signal Processing

• This paper presents our general strategy for designing learning machines as well as a number of particular designs. The search for methods allowing a sufficient level of adaptivity are based on two main principles: 1. Simple adaptive local models and 2. Adaptive model distribution. Particularly important concepts in our work is mutual information and canonical correlation. Examples are given on learning feature descriptors, modeling disparity, synthesis of a global 3-mode model and a setup for reinforcement learning of online video coder parameter control.

Keyword

Adaptive model distribution

Simple local models

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Artificiell Intelligens AI

TECHNOLOGY

TEKNIKVETENSKAP

Publication and Content Type

vet (subject category)

rap (subject category)