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We evaluate this new class of sequence-to-sequence models with continuous outputs on the task of neural machine translation.
The class herein considered is characterised by continuous inputs, continuous outputs and binary discrete inputs.
The proposed approach focuses the attention on the identification of a global model that predicts the continuous outputs of the HDS.
This paper addresses such identification problem, by focusing the attention on the identification of a global model that predicts the continuous outputs of the HDS.
In the first part of this paper, we consider arguments for utilizing continuous outputs of base classifiers versus categorical outputs and conclude: if one succeeds in having a small number of expert networks working perfectly in different parts of the input feature space, then crisp outputs may be preferable over continuous outputs.
The continuous outputs were used to compute the AUC metrics, and the AUC scores were used as an index of classification performance because they are robust to class imbalances and are better able to incorporate the relationship between probabilistic classifier output and discrete category membership.
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The PEMFC stack has a continuous output power of 12.5 W with a peak power output of 17 W.
A continuous output feedback control scheme rendering the closed-loop double integrator system globally stable in finite-time is presented.
Then, a continuous output feedback control law is proposed for nominal double-integrator system and its perturbed version.
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