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20 Feb 2018: kA(a,a′) = δa(a. ′), (7). where δa(a′) = 1 iff a = a′, 0 otherwise.4.2. ... The kernelfunction between two sets of actions is. kA(aB,aE) = δaB(a.

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20 Feb 2018: kA(a,a′). For a sequence of belief state-. action pairs Bt = [(b0,a0),. ... For the action space kernel, the δ-kernel is useddefined by:. kA(a,a′) = δa(a. ′).

20 Feb 2018: and action spaces kB(b,b′)kA(a,a′). ... kA(a,a′) = δa(a. ′) (5). where δa(a′) = 1 iff a = a′, 0 otherwise.

20 Feb 2018: and action spaces kB(b,b′)kA(a,a′). ... kA(a,a′) = δa(a. ′) (5). where δa(a′) = 1 iff a = a′, 0 otherwise.

20 Feb 2018: Q(b,a) GP (0,k((b,a), (b,a))) (2). where the kernel k(, ) is factored into separate kernels overbelief and action spaces kB(b,b′)kA(a,a′). ... kA(a,a′) = δa(a. ′) (7). where δa(a′) = 1 iff a = a′, 0 otherwise.

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20 Feb 2018: k(b,a,b′,a′) = kB(b,b′)kA(a,a. ′). (3). In addition, if we assume additive noise in the Q-function, Q(b,a) N(0,σ2), the following ... kA(a,a′) = 1 δa(a′) (14). where δa(a′) = 1 iff a = a′.For the full-space systems, the kernel function

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.

13 Mar 2018: ÃÄ-,90/ªÀ=4"!t4M9sÌ:4ÊtA%&Ì:C=90A'4<UKtæ4A'?&>-,"$+?A'Ê$&,&ÁÆ646A'ûtÀM&ADBAD/QCM#'&/&4<?&>5ÚÌ:Æ6A'ÁÆ8-?&>Á9sÊ0&-UX+,Æ6&/Q"wÉtAD/kÀ=/#%A#!w&#%ADÆM9t9M52&,? ... ÀM+:,&4A'?&-U"w+,A%9<46Ì}&-,&46&&56&58+?9ËE4658UÆM& /A'4MAD/kÀ=/K.

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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′).

20 Feb 2018: bbj¢&}M"@"jj}j[&}(¢}M|ªjb}a{(|&}(}M¢¤&Ka"}Xl|&|s¡"&}¡}bj¤&|@b&¤&@}"|&}b|&bj5¢@¤[@¤&}"¤|&| b9}&}M¤@j@b

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).

13 Mar 2018: åãòIë5àóáãçákîïçåQä)ðäÎàAìç éäçà¡áå èùäåá èìèàïAàøAèé áãèâåäÎáQïçáQäòà ë5ãèéèáãèÕèùÒçñáäÎàAìçéäç à¡áøAò¡èåàòálèù¡äåá]'äÎàÒöDò ... èéHøAèéä)ì%çá

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

13 Mar 2018: a keypoint kA in A which matches kBin B is kept if kB matches kA and vice versa).

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: v'$px kÃ Æ ÊV kÃ Æ ÙV3¢{}|'«'=HwbVz{wbxvV1{}|]|vzvVVwbH=|@£&w}kH{}{}|kXvV3vHwkxlÃ/{vg{}|vx'l Å.

20 Feb 2018: Zhang and Chaudhuri2015] Chicheng Zhang and Ka-malika Chaudhuri. 2015. Active learning fromweak and strong labelers.

13 Mar 2018: a keypoint kA in A which matches kBin B is kept if kB matches kA and vice versa).

20 Feb 2018: kA(a,a′). The. policy is optimised using an algorithm called GP-SARSA [7, 45] in which theQ-function is updated by calculating the posterior given the collected belief-action pairs ... The summary action kernel is defined as:. kA(a,a′) = δa(a. ′)

13 Mar 2018: A Probabilistic Framework for Space Carving. A. Broadhurst, T.W. Drummond and R. CipollaDepartment of EngineeringUniversity of CambridgeCambridge, UK CB2 1PZ. Abstract. This paper introduces a new probabilistic framework forSpace Carving [9]. In

13 Mar 2018: v'$px kÃ Æ ÊV kÃ Æ ÙV3¢{}|'«'=HwbVz{wbxvV1{}|]|vzvVVwbH=|@£&w}kH{}{}|kXvV3vHwkxlÃ/{vg{}|vx'l Å.

13 Mar 2018: A Probabilistic Framework for Space Carving. A. Broadhurst, T.W. Drummond and R. CipollaDepartment of EngineeringUniversity of CambridgeCambridge, UK CB2 1PZ. Abstract. This paper introduces a new probabilistic framework forSpace Carving [9]. In

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20 Feb 2018: Q(b,a) GP (0,k((b,a), (b,a))). (41). The kernel k(, ) is often factored into separate kernels over the belief state and actionspaces kB(b,

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20 Feb 2018: and action spaces kB(b, b′)kA(a,a′)and Qπ(b,a) N(0,σ2) is Gaussian noise, Gaussian pro-cess regression [22] can be applied to find the posterior

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