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9 Sep 2013: σ ,. 〈logl,. αTλll. 〉. (16). Notice that the last element of the tuple now is a scalar value, which makes the op-erations and (which are similar to (15) and straightforward to

13 Mar 2018: 22WVlA = -V2U. (4). Here V and V2 are dimensionless projection op-erators involving combinations of projections intoimage and surface tangent planes.

13 Mar 2018: "!# $%& %'()#,&-##)./10324 56&-786&9: ;#<: =;#: >?@-<;A66"B9: ;#<: =;#: >? C<DFEGHIJBGKMLONQPLSRLTU(VQLP LXWZYQ[&R]ARR[&T_T_UVALO]QRLOBaNFLPSL=NQU ]bWBcdP ]QNA[eTAdfUf[&ghNQPBiLRU P]QSU]QP LjWBTQYhLdghU[&ThPLBiLP kXWlc&dP

13 Mar 2018: LeCun. Sceneparsing with multiscale feature learning, purity trees, and op-timal covers.

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13 Mar 2018: path-length. to train data i.e. good generalisation, however, it is not op-timal in classification time. ... 2008). Random kitchen sinks: replacing op-timization with randomization in learning. In Proc.

13 Mar 2018: engineering or graph op-timisation. ... This demonstrates that learning with the op-timum scale factor leads to the convnet uncovering a more accuratepose function.

20 Feb 2018: However,approx-imate solutions can still provide useful policies. The simplest ap-proach is to discretise belief space and then use standard MDP op-timisation methods [6].

13 May 2004: Cluster Voting for Speaker Diarisation. S.E. TranterCUED/F-INFENG/TR-476. 1st May 2004. Abstract:. It is often important to be able to automatically detect ‘who spoke when’ in audio data. The speaker di-arisation task attempts to address this

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