268 research outputs found
Exploring the Influence of Wind Turbine\u27s Blades on Its Output and Efficiency
Wind turbines are machines that convert wind energy into electricity. The efficiency of this conversion is measured by comparing the incoming wind\u27s speed and the output power. This paper focuses on how the properties of blades affect the output and power of wind turbines. The attributes of turbine blades that affect output and efficiency, such as blade size and angle of entry, are considerable. Although results generally match with theory models, we find a size limit with blade length
Improved Dropout for Shallow and Deep Learning
Dropout has been witnessed with great success in training deep neural
networks by independently zeroing out the outputs of neurons at random. It has
also received a surge of interest for shallow learning, e.g., logistic
regression. However, the independent sampling for dropout could be suboptimal
for the sake of convergence. In this paper, we propose to use multinomial
sampling for dropout, i.e., sampling features or neurons according to a
multinomial distribution with different probabilities for different
features/neurons. To exhibit the optimal dropout probabilities, we analyze the
shallow learning with multinomial dropout and establish the risk bound for
stochastic optimization. By minimizing a sampling dependent factor in the risk
bound, we obtain a distribution-dependent dropout with sampling probabilities
dependent on the second order statistics of the data distribution. To tackle
the issue of evolving distribution of neurons in deep learning, we propose an
efficient adaptive dropout (named \textbf{evolutional dropout}) that computes
the sampling probabilities on-the-fly from a mini-batch of examples. Empirical
studies on several benchmark datasets demonstrate that the proposed dropouts
achieve not only much faster convergence and but also a smaller testing error
than the standard dropout. For example, on the CIFAR-100 data, the evolutional
dropout achieves relative improvements over 10\% on the prediction performance
and over 50\% on the convergence speed compared to the standard dropout.Comment: In NIPS 201
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