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  1. Results that match 1 of 2 words

  2. 9 Aug 2005: 6 /J/,. 6 (7@5KLM N0/ OP(!Q@O R. S7T0UGTWV:X?Y[Z]T0_a[X?V:X:YDYDTWYbBV:c?S7V:X?dbdbZUeSfgZhij_GZk7Z_7TDlX?U7_l]mcUonpGTWVGqhBiGTsrtTWT0rYDTWrtV:cvuxwzyowLhBTWd. ... OP/).O=$(: 4+,G) zK $'&D=(? 4+A2+4=1[,4A1@2 )? 3 #43,e O#@4+=/ =1 4+= /;4+4 ,42'2+),1;,,G/
  3. 20 Feb 2018: the Q-function estimatewe expect during the process of learning and H is a linear op-erator that captures the reward lookahead from the Q-function(see Eq.
  4. Learning a Kinematic Priorfor Tr ee-Based Filterin g A. ...

    mi.eng.cam.ac.uk/reports/svr-ftp/thayananthan_bmvc03.pdf
    23 Nov 2003: PQ. RRSST>TU>U VWX>XY>YZ>Z[] >_ >abc. d>defg hi jk. l>lm. n>no>op>pq. rs ttuu.
  5. ./plot_entropy.eps

    mi.eng.cam.ac.uk/~ar527/chen_is2017.pdf
    15 Jun 2018: In standard systems this issue would be addressed by op-timising the language model scale factor.
  6. Low-Resource Speech Recognition and Keyword-Spotting

    mi.eng.cam.ac.uk/~mjfg/SPECOM_2017.pdf
    29 Nov 2017: 56/63. Summary plot MTWV vs TER for FLP (OP-2). • Examined performance with a consistent configuration57/63.
  7. CAMBRIDGE UNIVERSITYENGINEERING DEPARTMENT ��������� ��…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/freitas_tr313.pdf
    9 Aug 2005: jRAKJP7 uq?et 9&;m_&5;q]5 oP;et4optqBN917R?e<9msPBe9dtX%?=<9dl EAKyHg4tO;=>? ... iMAM?e>Ht4JPBmAM?7:>o 9;=9Jq?o 9;=>@t:7<B? Jus<bH A jAOJP7uq?=t 9;0 B AKo<oP;etqAO[I C4?e<9T?
  8. 20 Feb 2018: increases. Pop op-erations are then performed where possible, the tree is prunedand identical nodes are joined so that the number stays constantor decreases. ... Error bars indicate 99% con-fidence intervals. This demonstrates the competitiveness of the
  9. 1 000 001 002 003 004 005 006 007 ...

    mi.eng.cam.ac.uk/~ar527/malinin_acl2017.pdf
    19 Apr 2017: A rejection curve derived froma model should sit between the random and op-timal curves.
  10. 20 Feb 2018: A comparison between the three op-tions is included in the experimental evaluation. ... whilst suffering initially.We hypothesise that the optimised SL pre-trainedparameters distributed very differently to the op-timal A2C ER parameters.
  11. IB-descriptors.dvi

    mi.eng.cam.ac.uk/~cipolla/lectures/PartIB/old/2013-IB-handout3.pdf
    13 May 2013: The way to achieve this robustness is to utilize in-terest points in computing the descriptor as op-posed to the raw image data.

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