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13 Feb 2023: 0.003. 0.01. 0.03. 0.1. 0.3. 1randomvariance. 50 100 150 200 250 300 350 400 450 500. ... 140. Active Learning with Statistical Models. training set size50 100 150 200 250 300 350 400 450 500.

27 Jan 2023: We generated a 200-step time series of 10-dimensional data from three models:5(a) a factor analyser (i.e.

13 Feb 2023: Figure 3d shows re-sults for one of the approximately 200 genes that were identified as differentially expressedright from the start of the time series.

13 Feb 2023: Theanalogous priors are used for both other datasets. The EachMovie dataset was preprocessed, first by removing movies rated by less than 15people, and people who rated less than 200 movies.

27 Jan 2023: 0. 50. 100. 150. 200. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10. ... 50. 100. 150. 200. a. bFigure 7. Histograms of the average activity of the top level binary unit, after prolongedGibbs sampling, when shown novel handwritten twos and threes.

13 Feb 2023: 1 0.5 0 0.5 1 1.50 50 100 150 200 250 300. ... 0. 0.2. 0.4. 0.6. 0.8. Parameters. f. 0 100 200 300 400 500 6000.

27 Jan 2023: 0 50 100 150 200. 0. 0.2. 0.4. 0.6. 0.8. 1.

13 Feb 2023: HM. C. Training RMSE. 0 10 20 30 40 0 50 100 150 200.

27 Jan 2023: is 100NN. For all datasets, we use the smallest m = 200 eigenvalue and eigenvector. ... set size Order Field σ = 200. 10 95.7 8.9 93.9 12.0 63.1 15.8 65.8 22.8 93.2 6.8 53.6 5.5

13 Feb 2023: 0.70, which is still betterthan the 26.61 0.65 error rate obtained for 25 trainingimages and 200 pixels per image (for a total of 5000 pix-els), cf. ... Table 1. Error rate performance on test set of 143 images when training set size varies, N {25, 50,