The definition assumes that documents are represented as length-normalized vectors in a real-valued space in the familiar way.
The mapped grasps are parameterized as feature vectors in a high-dimensional space.
The mapped grasps are represented by feature vectors in a high-dimensional space.
Well, because we are going to try to use our new knowledge, let's try to do it using vectors in a smart way.
Several existing replicated systems already successfully use version vectors in a manner similar to the implementation we propose, implying that our guarantees can be layered on these systems.
This method first determines the knot vectors in a way that guarantees the existence of an interpolating B-spline.
Computers are proficient at calculating vectors in a 3D space and, as a result, attaching a bullet to a pixel and turning it into a game is relatively straightforward.
This approximate method consists in expressing the new frequency response function as a linear combination of vectors in a truncated modal basis.
We compute the n-cohomology of the K-finite vectors in a limit of a discrete series representation π of G.
In this article we extend these results for reciprocal preference relations originating from the pairwise comparison of random vectors in a machine learning context.
We next assessed the activity of SVP[Rapa] administered in combination with AAV vectors in a pilot study with nonhuman primates (NHP, Macaca fascicularis), using a similar strategy as described in Fig. 1a.
