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Incremental Learning of Locally OrthogonalSubspaces for Set-based…
mi.eng.cam.ac.uk/reports/svr-ftp/kim_bmvc06.pdf21 Sep 2006: w j ZT R j Z ' U jU Tj. From (2), we have w jU Ti U jU Tj Ui = O,i.e. -
Incremental on-line adaptation of POMDP-based dialogue managers…
mi.eng.cam.ac.uk/~sjy/papers/gktb14.pdf20 Feb 2018: 0j),(b,a)),. ,k((b. tj,a. tj),(b,a))]. T,j = 1,. , l.Therefore, in principle, one needs to be able to calculate thekernel function k((b′,a′),(b,a)) -
A Generalised Derivative Kernel for Speaker Verification C. Longworth …
mi.eng.cam.ac.uk/~mjfg/cl336_INTER08.pdf2 Mar 2009: K(Oi, Oj ) =1. TiTj. Ti. t=1. Tj. s=1. k(oit, ojs) (5). ... K(Oi, Oj ) =M. m=1. 1. ρmi ρmj. Ti. t=1. Tj. -
Principled Fusion of High-level Model and Low-level Cues for ...
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2008-CVPR-motion-segmentation.pdf13 Mar 2018: with,. po =K. k=1. {(T tkπk. ) k1. j=1. (1 T tj πj )}δ(mt=k). ... p({yt}Nt=1|Hs) =. t. j,k. τ tj. pl(yt1k , y. tj|τ. tj , µ. -
Bitext Alignment for Statistical Machine Translation
mi.eng.cam.ac.uk/~wjb31/ppubs/YDengDefenseDec05.pdf16 Feb 2008: Word alignment a = aJ1: saj tj , j = 1, 2, , J = hidden r.v.Conditional likelihood P(t, a|s) = complete dataSentence translation P(t|s) =. a P(t, a|s) = incomplete ... t1 t2 ….tj- tj tJtj-…. …. s1 ….si sI…. tj1. αj (i, φ, h) =i′,φ′,h′. -
Class-based language model adaptation using mixtures ofword-class…
mi.eng.cam.ac.uk/reports/svr-ftp/moore_icslp00.pdf2 Nov 2000: p j(wi) p(wi C(wi) Tj) p(C(wi) C(wi 1) C(wi 2) C(wi 3)) (3)where Tj is the jth topic, C(w) is the ... The models were com-bined by linear interpolation:. p(wi) tj 0 p(wi. -
Projective Bundle Adjustment from ArbitraryInitialization using the…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2016-ECCV-varpro.pdf13 Mar 2018: Now each point is typically parametrized as. x̃j := x̃(xj, tj) :=[x>j tj. ... xj1 xj2 xj3 tj. ]>(20). where xj =[xj1,xj2,xj3. ]>is the vector of unscaled inhomogeneous coordinates of. -
Estimating Disparity and Occlusions in Stereo Video Sequences Oliver…
mi.eng.cam.ac.uk/reports/svr-ftp/williams_cvpr2005.pdf9 Aug 2005: iI. Φ(xti; yt). i,jHΨh(xti, x. tj ). i,jVΨv(xti, x. tj ). iI. xt1i. Ψt(xti, xt1i )P (x. t1i |yt1,. , y1). (3). ... to the filtering model to give:. P (xt|y, Mf ) =. i,jHΨh(xti, x. tj ). i,jVΨv(xti, x. tj ). iI. Φ(xti; yt). -
��������� �� �� ��� ������������������� ������ ������ "!#�����$� …
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/leggetter_tr181.pdf9 Aug 2005: JKMW= RR<KJK,/; 238=@; =@R2 2,QGCR<>=@23,/M JT? ;N?= GCJi23JKGC, =@4R'JT?'.) , 2',/8043N =@M H ,/,/? ,2,Q?;,/; 28P M=? ;fA'M,/M =GP= JTGIAG <TJ ,Q<TJ88;80R23JTG ... a. U. ,GC,E=? L0,/23804 8= =@A'MMJ>=? ;JKM234JKHA23JK80? JKMl=@;= R2,/;CAMJK? =U<KJK?,E= 4 -
Principled Fusion of High-level Model and Low-level Cues for ...
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2008-CVPR-motion-segmentation.pdf13 Mar 2018: with,. po =K. k=1. {(T tkπk. ) k1. j=1. (1 T tj πj )}δ(mt=k). ... p({yt}Nt=1|Hs) =. t. j,k. τ tj. pl(yt1k , y. tj|τ. tj , µ. -
A Network-based End-to-End Trainable Task-oriented Dialogue System…
mi.eng.cam.ac.uk/~sjy/papers/wgmv17.pdf20 Feb 2018: tj). ᵀ log ptj, where ytj and p. tj are out-. -
SVMs, Generative Kernels & Maximum MarginStatistical Models Mark…
mi.eng.cam.ac.uk/~mjfg/ISM_talk.pdf9 Dec 2004: where Oi and Oj are sequences of length Ti and Tj respectively, and. -
��������� � ���� ������������������ ������������� �"!$#%� �…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/gee_tr77.pdf9 Aug 2005: TJ #O:U8289FW 4!$:9,3 T.:2O ,!W9O6 ' 9F6A8J! ( 82Fl!$6? 6B ' :L0! ... 43H F TJ 7 , : 8c2S!$82F%6!$0 ( 1:9F!G828 ' /A:2O! -
Projective Bundle Adjustment from ArbitraryInitialization using the…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2016-ECCV-varpro.pdf13 Mar 2018: Now each point is typically parametrized as. x̃j := x̃(xj, tj) :=[x>j tj. ... xj1 xj2 xj3 tj. ]>(20). where xj =[xj1,xj2,xj3. ]>is the vector of unscaled inhomogeneous coordinates of. -
Discriminative Adaptation for Speaker Verification C. Longworth and…
mi.eng.cam.ac.uk/~mjfg/longworth_INTER06.pdf22 Nov 2006: Ti and Tj are the lengths of utterances O(i) and O(j). -
Bitext Alignment forStatistical Machine Translation Yonggang Deng A…
mi.eng.cam.ac.uk/~wjb31/ppubs/YDengDissertationDec05.pdf16 Feb 2008: English) s = sm1. Note that each tj and si is a segment, which is to say a string that. -
��������� �� �� ������� ���������������������������…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/gales_tr284.pdf9 Aug 2005: AIAT@>AI(URRmIKBBlIAi)tJ>KbVTIAoT@"G'=ZmUIABcal@pc@H=jtUtUalRKIAi$=jpl@oT=?>ASU@T6NIAGU@(IApIABtPRTBcBlIAkU>A@RTa=?G IAGUtUSUpoT@pcRTaplRk9@=?GRFSUpl>AIA@a!ZIAp)$ac@BltP@pplR ... JRTatJa =?"plIAD=?>=jtUtU>AIAD=?pcIARTGUB0C UR(@oT@"aDCZ= -
Latent Intention Dialogue Models Tsung-Hsien Wen 1 * Yishu ...
mi.eng.cam.ac.uk/~sjy/papers/wmby17.pdf20 Feb 2018: tj is the last output token (i.e. a word,. a delexicalised2 slot name or a delexicalised2 slot value),and htj1 is the decoder’s last hidden state. -
Unsupervised Bayesian Detection of Independent Motion in Crowds…
mi.eng.cam.ac.uk/reports/svr-ftp/brostow_MotionInCrowdsCVPR06.pdf14 Sep 2006: This. was determined empirically as a conservative threshold. Tocompare two trajectories Xi and Xj , which respectively ex-tend in time over ti and tj , we consider only the over-lapping range ... of frames {fn : n ti tj}. -
Incremental Learning of Temporally-CoherentGaussian Mixture Models…
mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_SME06.pdf14 Mar 2006: 1i µ. Ti µ j C. 1j µ. Tj µ C1µ T.
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