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  2. nips7.dvi

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2005-ANIPS-Williams-VIC-algorithm.pdf
    13 Mar 2018: This is restrictive in practicein that test data may contain distortions that take it outside the strict ambit of the trainingpositives. ... This can be computed in terms of likelihoods. (1)so then the test becomes (2)where.
  3. 20 Feb 2018: During test-ing, we greedily selected the most probable intention andapplied beam search with the beamwidth set to 10 when de-coding the response. ... The significance test is based on atwo-tailed student-t test, between NDM and LIDMs.
  4. 20 Feb 2018: Bold values are statis-tically significant compared to non-bold values in the same groupusing an unpaired t-test with p < 0.01. ... The difference between bold valuesand non-bold values is statistically significant using an unpaired t-test where p < 0.02.
  5. Modelling Uncertainty in Deep Learning for Camera Relocalization Alex …

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2016-ICRA-pose-uncertainty.pdf
    13 Mar 2018: This is achieved by sampling the network withrandomly dropped out connections at test time. ... At test time we perform inference byaveraging stochastic samples from the dropout network.
  6. 20 Feb 2018: 2016a). Statisticalsignificance was computed using two-tailed Wilcoxon Signed-Rank Test ( p <0.05) to compare models w/and w/o snapshot learning. ... 0.540 0.559 0.459. Table 2: Average activation of gates on test set.
  7. Using Wizard-of-Oz simulations to bootstrap…

    mi.eng.cam.ac.uk/~sjy/papers/wiyo03.pdf
    20 Feb 2018: Sections 3 and 4 detail a method for addressing these issues, and the procedure used to test the method, respectively.
  8. Tracking Using Online Feature Selectionand a Local Generative Model…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2007-BMVC-Woodley.pdf
    13 Mar 2018: We perform the adapted online feature selection algorithm (see Alg. 1) on a numberof test sequences. ... We take a single test image, and create a test sequence by adding fixed size, randomlypositioned black squares to simulate occlusion.
  9. acl2010.dvi

    mi.eng.cam.ac.uk/~sjy/papers/gjkm10.pdf
    20 Feb 2018: functionsfrom Table 1.The intention was, not only to test which algo-rithm yields the best policy performance, but alsoto examine the speed of convergence to the opti-mal policy.
  10. Label Propagation in Video Sequences Vijay Badrinarayanan†, Fabio…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2010-CVPR-label-propagation.pdf
    13 Mar 2018: RESULTS AND DISCUSSIONSAccuracy test Fig. 6 reproduces the quantitative results ofthe tests on Seq 1, 2 & 3. ... The comparable test accuracy to training under ground truth provides support for trainingclassifiers using the proposed methods.
  11. The Effect of Cognitive Load on a Statistical Dialogue ...

    mi.eng.cam.ac.uk/~sjy/papers/gtht12.pdf
    20 Feb 2018: The averaged results are givenin Table 2. We performed a Kruskal test, followedby pairwise comparisons for every scenario for eachanswer and all differences are statistically signifi-cant (p < 0.03) apart

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