Sentence examples for matrix of connection from inspiring English sources
where W l denotes the matrix of connection weights between l−1-th and l-th layers.
The history of past processing is 'stored' in the (enduring but modifiable) matrix of connection weights of the neural network, and thus influences (in a causally holistic fashion) the ongoing processing of input (Rumelhart, Smolensky, McClelland, and Hinton 1986).
Starting with the DNN models trained using foreign data, the output layer is replaced by a new layer in which we randomly initialise the W L, which is the matrix of connection weights between the layer L−1 and the output layer L. The network is then retrained using the MP-FR data, which most closely matches the evaluation set.
The correlation matrix for each breed was then transformed into a matrix of connection strengths (i.e., an "adjacency" matrix) using a power function (connection strength = |correlation|b), which resulted in a "weighted" network.
The correlation matrix is then transformed into a weighted undirected network (i.e., a matrix of connection strengths) by raising the absolute value of each entry to a power β.
In this paper, we first provide integer linear programs and heuristic algorithms to solve the scheduling problem for a static traffic matrix (of connections) for both, the unconstrained TWIN and the TWSN networks and also compare their performances under a dynamic traffic scenario.
This matrix of connections preserves non linear associations among variables and captures connection schemes among clusters.
In particular, we considered a network of interacting neural populations, coupled by a specific matrix of connections.
This matrix of connections, as detailed by Buscema and Grossi (21), preserves nonlinear associations among variables and captures connection schemes among clusters.
In the analysis we used the polyform group of TUs to build a diagonal matrix of connections between FLs attributed to the TUs.
To determine whether the restriction also created structuring in which particular group of weights interacted preferentially with different subsets of phenotypes, we searched for modularity by applying the bipartite leading eigen vector approach to the matrix of connections from weight to phenotypes [ 33].
