Sentence examples for weights decay from inspiring English sources

Still, as Figure 4B shows these edge weights decay dramatically; the number of robust edges (those with a weight near unity) is a very small fraction of the total number of edges in the dense consensus graph.

A weighted sum where the weights decayed exponentially from 1 to 5 of this vector was then computed, yielding a composite index of the similarity of the local prior context of the 2 items.

In addition, we observe that prior to the sampling-bias-correction, the distribution of edge weights decays monotonically, whereas after, there is a bimodal distribution which is reminiscent of the observed histogram shown in Figure 2 for the random network inference score distribution.

Weight decay parameter (lambda = 0.0025)   5.

The weight decay is with the value of 0.0005.

C-NN; hidden nodes 57, learning rate 0.01, no weight decay needed, training epochs 35,000.

The weight decay parameter λ=1×10−4 and the maximum number of iterations was set to 200.

A-NN; hidden nodes 72, learning rate 0.02, weight decay 0.001, training epochs 30,000.

B-NN; hidden nodes 76, learning rate 0.02, weight decay 0.001, training epochs 30,000.

G-NN; hidden nodes 68, learning rate 0.015, weight decay 0.0005, training epochs 28,000.

H-NN; hidden nodes 81, learning rate 0.02, weight decay 0.001, training epochs 33,000.