In the existing literature, a common approach to approximate the metric is to first find a map that preserves Euclidean lengths instead of distances.

In this section we state some elementary results that we will use for our main proofs. The next Lemma is part of the proof of [44, Lemma 4.2], which we state here as a separate result to save some space from the longer proofs that follow later. This is part of the proof of [44, Lemma 4.2]. In this section we specialize the definitions to the case of Gaussian matrices. Lemma 7. Let n 1 be an integer, and δ (0, 1/2) .

In the existing literature, a common approach to approximate the metric is to first find a map that preserves Euclidean lengths instead of distances.

In this paper we present a new version of the standard multilayer perceptron (MLP) algorithm for the state-of-the-art in neural network VLSIimplementations: the Intel Ni1000.

Ni1000 was designed to calculate "city block" distances (Le.

Ni1000 was designed to calculate "city block" distances (Le.