Search

20 Feb 2018: kA(a,a′) = δa(a. ′) (5). where δa(a′) = 1 iff a = a′, 0 otherwise. ... 24, no. 4, pp. 562–588, 2010. [13] M Gašić, C Breslin, M Henderson, D Kim, M Szummer,B Thomson, P Tsiakoulis, and S Young, “POMDP-based dia-logue manager adaptation

27 Feb 2018: Resource-explicit models of bacterial mutualismscompare well with experiments in which mutualists are wellmixed [5,6,23,24].

20 Feb 2018: kA(a,a′). For a training sequence of belief state-action pairs B = [(b0,a0),. , ... kA(a,a′) = δa(a. ′) (6). where δa(a′) = 1 iff a = a′, 0 otherwise.

13 Mar 2018: áà-ØÙcä)Ü@ÝØàØÙ6ØäÙcÚä_ÙcÚàåfä_ØÞÛÝÜ@ÙcØáâSàÕ'Ü!Kä/WÕcß)Þ ØÙÜ@áà+)ÚÕ_Ø-ØÈáâÙcMÜ@ÙØÙàßÚäØáàÚÚàQâ)ÚáÚ ... áÙcÚ2Öß)ØÖÚß:äcÖ

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.

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.

7 Jan 2018: 14. Lighting and Shading (a quick refresher). Recall the classic lighting equation: I = kA kD(N•L) kS(E•R). ... shader. much, much more! 24. Antialiasing on the GPU. Hardware antialiasing can dramatically improve image quality.

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