Titlebook: Artificial Neural Networks; Methods and Applicat Petia Koprinkova-Hristova,Valeri Mladenov,Nikola K Conference proceedings 2015 Springer In

MAIM

Feministische Methodologien und Methodenow empirically that the proposed method overcomes the difficulty in training DBMs from randomly initialized parameters and results in a better, or comparable, generative model when compared to the conventional pretraining algorithm.

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Image Classification with Nonnegative Matrix Factorization Based on Spectral Projected Gradient,s in NMF become large-scale. However, the computational problem can be considerably alleviated if the modified Spectral Projected Gradient (SPG) that belongs to a class of quasi-Newton methods is used. The simulation results presented for the selected classification problems demonstrate the high efficiency of the proposed method.

cognizant

Learning Gestalt Formations for Oscillator Networks,o decided whether input features belong to a common group or have to be separated. The technique is evaluated within different perceptual grouping scenarios and with two kinds of artificial neural networks.

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CBC471

Learning to Look and Looking to Remember: A Neural-Dynamic Embodied Model for Generation of Saccadieneration of motor signal, adaptation of gaze shift’s amplitude, memory formation, scene exploration, and the coordinate transformations. We demonstrate the functioning of the architecture on a simulated robotic agent and provide a discussion of its implications in terms of neural-dynamic and cognitive modelling.

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How to Pretrain Deep Boltzmann Machines in Two Stages,ow empirically that the proposed method overcomes the difficulty in training DBMs from randomly initialized parameters and results in a better, or comparable, generative model when compared to the conventional pretraining algorithm.

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