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20 Feb 2018: is factored into separate kernels over thesummary state and action spaces kC(c,c′)kA(a,a′). ... 3.3. The agenda-based simulated user. The agenda-based user simulator [24, 25] factorises the user stateinto an agenda and a goal.

20 Dec 2018: 18. 7 Hamming’s breakthrough 20. 8 General considerations 24. 9 Some elementary probability 27. ... 24 Exercise Sheet 1 85. 25 Exercise Sheet 2 90. 26 Exercise Sheet 3 95.

20 Feb 2018: The summary action kernel is defined as:. kA(a,a′) = δa(a. ′) (3). ... 24. Figure 13: The number of times each system queries the user for feedback during on-linepolicy optimisation as a function of the number of training dialogues.

20 Feb 2018: 211. 2121. 1. aannna. annnnn. ka. rd (13). Here nr specifies the number of events that occurred r times and a fixed discounting factor was used if they are zero. ... 52 class n-gram 26.3 25.0 24.9 HVS_52 21.7 20.4 20.1. Table 1: Perplexity for models of

20 Jun 2018: Verso, written in black ink,24 lines, parallel to the fibres. Text:RECTO. ... Text:ScriptFinal 'alit extends below the connecting stroke. llm, }pa' and ka' are usuallywritten with a single curved stroke.

20 Feb 2018: function,Q(b, a) GP (0, k((b, a), (b, a))) wherethe kernelk(, ) is factored into separate kernels over thesummary state and action spaceskB(b, b)kA(a, a). ... 24, no. 4, pp. 562–588, 2010. [10] M. Gǎsić, S. Keizer, F.

8 Feb 2018: International Finance, Pembroke Visiting. 2017. Turalay Kenc (to 24 Mar. 2018) (Visiting Professorship). ... Oncological Pathology. 2003. Ming-Qing Du, HH. Oncology, Li Ka Shing. 2015.

20 Feb 2018: To obtain a closed formsolution of (24), the policy π must be differentiable with respect to θ. ... 10. To lower the variance of the estimate of the gradient, a constant baseline, B, can beintroduced into (24) without introducing any bias [22].

31 Dec 2018: 23. 1. z. 4p2+. (3.24). The next part’s a little fiddly. ... 1. 1. 2p2. 3N. V+. which we then substitute into (3.24) to get.

20 Feb 2018: An advantage of this sparsification approach is that itenables non-positive definite kernel functions to be used in theapproximation, for example see [24]. ... It has already beenshown that active learning has the potential to lead to fasterlearning [24]