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13 Mar 2018: 21] for upper bodypose estimation. In [24] it is suggested to partition the parameter spaceof a 3D hand model using a multi-resolution grid. ... range. At detection rates of 0.99 the false positiverate for the centre template is 0.24, wheras it is

13 Mar 2018: While some works like [8, 18, 24] have resortedto low-rank factorization of weight matrices between twogiven layers, others have used sparsification methods forthe same [6]. ... 2. [24] J. Xue, J. Li, and Y. Gong. Restructuring of deep neuralnetwork

13 Mar 2018: Puzicha. Shape matching and object recognition using shape contexts. PAMI,24(24):509–522, 2002. ... PAMI, 24(5),2002. [4] P. Dollár, Z. Tu, H. Tao, and S.

3 Jul 2018: training set of Aurora4. Model A B C D AVGDNN/clntr 2.71 43.00 24.06 58.66 45.48VDCNN-max-4FC/clntr 2.32 35.99 21.20 ... 24, no. 12, pp. 2263-2276, Dec. 2016. Contact: j.m.rownicka@sms.ed.ac.uk.

13 Mar 2018: Using artificial data [24, 9] provides an-other alternative for obtaining large amounts of high qualitylabelled data. ... 47.4 80.1 9.31 12.0 75.3 66.2 91.8 53.8Dilation Network [36] H 24K 96.8 73.7 86.4 34.0 63.4

20 Feb 2018: 24, pp. 562–588, 2010. [20] M. Lukoeviius and H. Jaeger, “Reservoir computing approachesto recurrent neural network training,” Computer Science Review,vol. ... abs/1412.2306, 2014. [24] G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L.

20 Feb 2018: When errors are correlated belief tracking is less accurate be-cause it tends to over-estimate alternatives in the N-best list[24]. ... 24, no. 4, pp. 562–588, 2010. 17. T. Minka, “Expectation Propagation for Approximate Bayesian Inference,” in

13 Mar 2018: 24. Brostow, G. J., Cipolla, R., 2006. Unsupervised bayesian detection of inde-522pendent motion in crowds. ... Image Vision Comput. 24 (8), 795–809.578. Messing, R., Pal, C., Kautz, H.

13 Mar 2018: This, however, requires setting of a learning rate. Ye et al.[24] have proposed an incremental version of LDA, which caninclude a single new data point in each time step. ... California, Dept. Statistics, Berkeley, CA,2005, Tech. Rep. 688. [24] J. Ye, Q.

13 Mar 2018: 1.99%. 0.05%. 1.38%. 60.43%. 69.01%. 60.09%. 89.85%. 75.19%. 87.24%. 1.65%. 0.83%. ... The overall score for TextonBoost in training and testing with daytime subsets of our database is 75.02%, and for dusk it was72.24%.