Coherence net: A new model of generative cognition
We propose a new algorithm and formal description of generative cognition in terms of the multi-label bagof-words paradigm. The algorithm, Coherence Net, takes its inspiration from evolutionary strategies, genetic programming, and neural networks. We approach generative cognition in spatial reasoning as the decompression of images that were compressed into lossy feature sets, namely, conditional probabilities of labels. We show that the globally parallel and locally serial optimization technique described by Coherence Net is better at accurately generating contextually coherent subsections of the original compressed images than a competitive, purely serial model from the literature: Coherencer.
|Keywords||Bag-of-words, Evolutionary algorithms, Generative cognition, Machine learning, Multi-label|
|Conference||6th International Conference on Evolutionary Computation Theory and Applications, ECTA 2014, Part of the 6th International Joint Conference on Computational Intelligence, IJCCI 2014|
Vertolli, M.O. (Michael O.), & Davies, J. (2014). Coherence net: A new model of generative cognition. In ECTA 2014 - Proceedings of the International Conference on Evolutionary Computation Theory and Applications (pp. 308–313).