Sentence examples for comparing the gradient from inspiring English sources
This can be noticed when comparing the gradient of the buffer size (Figure 7) to the gradient of the latency (Figure 11).
Perform non-maximum suppression of the gradient amplitude by comparing the gradient magnitude of the adjacent pixels and keeping the points at which the maximum local variation of the amplitude appear.
On the other hand, we can apply (3.2) to determine whether (u_{8}) approximates the solution of Problem II, mainly by comparing the gradient field of (w_{0}) with the vector field of (vert nabla u_{8} vert ^{p-2}nabla u_{8}), depicted in Figure 5.
Comparing the gradient of each variable, when (Wleq frac {1}{2M+1}), the steepest descent of the objective function is along the decreasing value of (nu _{1}^{prime }) with θ 1=μ 1=σ 1=1, for r sum≤W α s.
Edge-based line averaging is done by interpolation along the edge direction, by comparing the gradients of various directions.
Once a node (s) has a packet to send toward sink i, it calculates and compares the gradient field of its neighbors v (vє nbr(s)) in response to each sink i following Equation 7. {V}_d^i v +betaem =kern0.5em {V}_d^i(v)+beT^i v}_T^i(v) (7 where β is the weighted factor of traffic cost and ( {V}_d^ikern0.24em (v) ) is the depth gradient field with the hop number in response to sink i.
The analyst may then want to objectively compare the gradient associated with each of the calibration curves in order to determine statistically if they are operating to the same efficiency.
Finally, we applied the relationship given below to compare the gradients of risk.
The above technique may be usefully applied in further situations to objectively compare the gradients associated with two calibration curves.
To investigate group differences in the rates of learning word sequence, we compared the gradients of the repeated trials (i.e. learning rates) of the three groups by including the gradients of the non-repeated trials as a covariate in an analysis of covariance (ANCOVA).
Reduction in number of steps required to converge to final value when comparing the pure gradient search algorithm with a gradient search algorithm aided by a fast algorithm starting point.
