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

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書目名稱Artificial Neural Networks影響因子(影響力)




書目名稱Artificial Neural Networks影響因子(影響力)學(xué)科排名




書目名稱Artificial Neural Networks網(wǎng)絡(luò)公開度




書目名稱Artificial Neural Networks網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Neural Networks被引頻次




書目名稱Artificial Neural Networks被引頻次學(xué)科排名




書目名稱Artificial Neural Networks年度引用




書目名稱Artificial Neural Networks年度引用學(xué)科排名




書目名稱Artificial Neural Networks讀者反饋




書目名稱Artificial Neural Networks讀者反饋學(xué)科排名

過份艷麗

Image Classification with Nonnegative Matrix Factorization Based on Spectral Projected Gradient, developed for this purpose. In a majority of them, the training process is improved by using discriminant or nearest-neighbor graph-based constraints that are obtained from the knowledge on class labels of training samples. The constraints are usually incorporated to NMF algorithms by ..-weighted p

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Energy-Time Tradeoff in Recurrent Neural Nets,in is quite sparse (with only about 1% of neurons firing). This complexity measure has recently been introduced for feedforward architectures (i.e., threshold circuits). We shortly survey the tradeoff results which relate the energy to other complexity measures such as the size and depth of threshol

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Analysing the Multiple Timescale Recurrent Neural Network for Embodied Language Understanding,research. Recently, researchers claimed that language is embodied in most – if not all – sensory and sensorimotor modalities and that the brain’s architecture favours the emergence of language. In this chapter we investigate the characteristics of such an architecture and propose a model based on th

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Learning to Look and Looking to Remember: A Neural-Dynamic Embodied Model for Generation of Saccaditation in a motor signal, which moves the eye to center the target object in the field of view. Looking facilitates memory formation, bringing objects into the portion of the retinal space with a higher resolution. It also helps to align the internal representations of space with the physical enviro

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How to Pretrain Deep Boltzmann Machines in Two Stages,lly that it is difficult to train a DBM with approximate maximum-likelihood learning using the stochastic gradient unlike its simpler special case, restricted Boltzmann machine (RBM). In this paper, we propose a novel pretraining algorithm that consists of two stages; obtaining approximate posterior

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