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The mean and variance of squared correlation for the proposed model and deep neural network are 0.618 ± 0.407e − 4 and 0.485 ± 4.82e − 4, respectively.

Then, what is the relationship between the three mechanisms and models to implement GrC such as fuzzy set, rough set, quotient space, cloud model, and deep learning?

In addition, this paper also focuses on state-of-the-art ASR techniques such as discriminative training of acoustic models including the Gaussian mixture model, subspace Gaussian mixture model, and deep neural networks, as well as various feature transformation techniques.

We propose the three mechanisms of MGrC and discuss their relationship with five major models of MGrC, i.e., fuzzy set, rough set, quotient space, cloud model, and deep learning.

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"Embedded Systems Modeling and Deep Compositionality".

Topics include big data and database management, interactive visualizations, nonlinear statistical models, and deep learning.

Her work at Columbia is about combining probabilistic graphical modeling and deep learning to design better sequence models.

The techniques presented here are general,include both graphical models and deep learning approaches,and can be adapted to a range of planners.

Haynes, W. I. & Haber, S. N. The organization of prefrontal-subthalamic inputs in primates provides an anatomical substrate for both functional specificity and integration: implications for Basal Ganglia models and deep brain stimulation.

Because in unusual and stressful situations, experience — the kind of experience business people gather over years in the market –ceases to be a helpful guide, whereas models and deep historical knowledge have at least a hope of getting at what's really going on.

Ongoing work focuses on generalizing the performance via hierarchical graphical models and deep learning approaches.

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