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Hierarchical Dialogue Management
https://www.mlmi.eng.cam.ac.uk/files/gordaniello_dissertation.pdf30 Oct 2019: k((bt,at),(bt,at))aTt k̃t1(bt,at) > ν (2.24). where. k̃t1(bt,at) = [k((bt,at),(b̃0,ã0)),.,k((bt,at),(b̃m,ãm))]T. at = K̃1t1k̃t1(bt,at). ... 24 Methods. Figure 3.7. Architecture of the BCM for a set of six domains. -
Optimising spoken dialogue systems using Gaussianprocess…
https://www.mlmi.eng.cam.ac.uk/files/thomas_nicholson_8224691_assignsubmission_file_done.pdf30 Oct 2019: 24. Reducing action selection complexity. 25Clustering of actions. 26. Cold Start. ... The authors use Rollout Classification Policy Itera-tion[24] (RCPI), policy iteration approach that generate training examples by using Monte-Carlo(MC). -
Bayes By Backprop Neural Networks forDialogue Management Christopher…
https://www.mlmi.eng.cam.ac.uk/files/tegho_dissertation.pdf30 Oct 2019: kB(b,b′)kA(a,a. ′) (2.17). The prior for the residual follows Q(b,a) N(0,σ2). ... minibatch. Using Monte Carlo sampling, the expression in 3.15. 24. can be written as:.
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