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TPAMI-0554-0706-2 1..14
mi.eng.cam.ac.uk/~cipolla/publications/article/2007-PAMI-Kim.pdf13 Mar 2018: We used 18 randomlyselected training/test combinations of the sequences forreporting identification rates. ... The test recognition rates changed byless than 1 percent for all of the different trials of randompartitioning. -
Learning to Track with Multiple Observers Björn StengerComputer…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2009-CVPR-hand-tracking.pdf13 Mar 2018: The running of tests consisting of all possible combina-tions of all trackers on all test sequences would take a pro-hibitive amount of time to complete. ... In order to test the validity of such a setup, weperformed tests using the complete tracking -
Using Wizard-of-Oz simulations to bootstrap…
mi.eng.cam.ac.uk/~sjy/papers/wiyo03.pdf20 Feb 2018: Sections 3 and 4 detail a method for addressing these issues, and the procedure used to test the method, respectively. -
DISTRIBUTED DIALOGUE POLICIES FOR MULTI-DOMAIN STATISTICAL…
mi.eng.cam.ac.uk/~sjy/papers/gkty15.pdf20 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. -
Latent Intention Dialogue Models Tsung-Hsien Wen 1 * Yishu ...
mi.eng.cam.ac.uk/~sjy/papers/wmby17.pdf20 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. -
acl2010.dvi
mi.eng.cam.ac.uk/~sjy/papers/gjkm10.pdf20 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. -
The Effect of Cognitive Load on a Statistical Dialogue ...
mi.eng.cam.ac.uk/~sjy/papers/gtht12.pdf20 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 -
Conditional Generation and Snapshot Learning inNeural Dialogue…
mi.eng.cam.ac.uk/~sjy/papers/wgmr16a.pdf20 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. -
Tracking Using Online Feature Selectionand a Local Generative Model…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2007-BMVC-Woodley.pdf13 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. -
SegNet: A Deep Convolutional Encoder-Decoder Architecture for…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2015-arxiv-SegNet.pdf13 Mar 2018: We test the performance of SegNet on outdoorRGB scenes from CamVid, KITTI and indoor scenes fromthe NYU dataset. ... Features based on appearance[32], SfM and appearance [2, 36, 20] have been explored forthe CamVid test.
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