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Thus we envision future Evolutionary CAD systems, EvoCADs. First, each word \(w_{t-i}\) (represented with an integer in \([1,N]\)) in the \(n-1\)-word context is mapped to an associated \(d\)-dimensional feature vector \(C_{w_{t-i}}\ ,\) which is column \(w_{t-i}\) of parameter matrix \(C\ .\) Vector \(C_k\) contains the learned features for word \(k\ .\) Let vector \(x\) denote the concatenation of these \(n-1\) feature vectors: \[ x = (C_{w_{t-n+1},1}, \ldots, C_{w_{t-n+1},d}, C_{w_{t-n+2},1}, \ldots C_{w_{t-2},d}, C_{w_{t-1},1}, \ldots C_{w_{t-1},d}). \] The probabilistic prediction of the next word, starting from \(x\) is then obtained using a standard artificial neural network architecture for probabilistic classification, using the softmax activation function at the output units (Bishop, 1995): \[ P(w_t=k

Format: Paperback

Language: English

Format: PDF / Kindle / ePub

Size: 13.36 MB

Downloadable formats: PDF

Thus we envision future Evolutionary CAD systems, EvoCADs. First, each word \(w_{t-i}\) (represented with an integer in \([1,N]\)) in the \(n-1\)-word context is mapped to an associated \(d\)-dimensional feature vector \(C_{w_{t-i}}\ ,\) which is column \(w_{t-i}\) of parameter matrix \(C\ .\) Vector \(C_k\) contains the learned features for word \(k\ .\) Let vector \(x\) denote the concatenation of these \(n-1\) feature vectors: \[ x = (C_{w_{t-n+1},1}, \ldots, C_{w_{t-n+1},d}, C_{w_{t-n+2},1}, \ldots C_{w_{t-2},d}, C_{w_{t-1},1}, \ldots C_{w_{t-1},d}). \] The probabilistic prediction of the next word, starting from \(x\) is then obtained using a standard artificial neural network architecture for probabilistic classification, using the softmax activation function at the output units (Bishop, 1995): \[ P(w_t=k

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