Sentence examples for more efficient representations from inspiring English sources
Robustness to noise informs on the efficiency of a representation for a given task: More efficient representations will typically tolerate more noise for a given level of performance (here, 75% accuracy).
However, latitude for interpretation can also be created by syllabaries when semantic units are broken into smaller parts for more efficient representation.
Single view plus depth is usually a more efficient representation of a 3D scene than two-channel stereo.
In another approach, floating control points are used in conjunction with generalized Delauney triangulation for a more efficient representation [17].
Furthermore, the deep structure of DNNs allows for a more efficient representation of many nonlinear transformations due to many layers of simple nonlinear processing.
The advantage of a DNN over one-hidden-layer NN is that the deep structure of the DNN allows much more efficient representation of many nonlinear transformations/functions [22].
Although the direct methods provide accurate predictions of one- and two-dimensional low Mach number flows, any future extension to more complex flows (gas mixtures, polyatomic gases, three dimensional geometries) is essentially related to the possibility of reducing the memory demand by a more efficient representation of the distribution function in the phase space.
Suppression of the neural response potentially allows the system to save resources and might reflect a more efficient representation [35].
Arena3D was chosen for 3D and more efficient representation of either the whole network of interactions or dense subparts of it.
Increases may indicate neural recruitment, while decreases may imply that a region became unimportant or developed a more efficient representation of the skill.
This could involve the representation of large spatial regions as if they were texture (essentially preparing a statistical description [ 47, 48]), a process that could allow a more efficient representation of information given the low spatial sampling and high featural uncertainty of the periphery.
