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27 Jan 2023: We restrict. ourselves to transduction, i.e., the unlabeled data Xu is also the test data. ... the results, we perform paired t-test on test accuracy. We highlight the best accu-.

13 Feb 2023: However, since sometimes the prior knowledge on the class label can be misleading, we need to test the robustness of the 'EMPrior Model' to such noisy prior knowledge. ... We varied the number of added classes to test reliability of our algorithm. •

13 Feb 2023: The classical approach used in our previous work tests each‘gene–gene’ interaction by doing a hypothesis test compar-ing the bootstrap confidence interval of each parameter to. ... ALGORITHMVariational Bayesian learningThe classical approach tests

27 Jan 2023: The RFP for a protein is defined as the fraction ofnegative test proteins (i.e. ... All of the models considered dramaticallyoutperform BLAST2 in this test of remote homologrecognition.

13 Feb 2023: posteriordensity function conditional on the training datax, yand the test pointsx. ... 2.2.1. Prediction at a random input If we now as-sume that the test inputx has a Gaussian distribution,x N (µµµx , ΣΣΣx ), the predictive distribution is

13 Feb 2023: If the modelcomplexity is either too low or too high performance on an independent test set will suffer,giving rise to a characteristic Occam’s Hill.

27 Jan 2023: For both data sets we use the standardtraining/test split, but for RCV1 since the test part of corpus is huge (around 800k docu-ments) we only randomly sample 10k as ... our test set.

13 Feb 2023: Note that xt1serves as a test input from the perspective of the GP regression model. ... HYPOTHESIS THAT THE OTHER FILTERS ARE BETTER THAN THE GP-ADF USING A ONE-SIDED T-TEST.

13 Feb 2023: We test all three samplers on 36 random graphs from each of the two generating processes, usingdifferent values of c1 and c2 for each random graph. ... of messages. Our test set uses a subset of the messages which are small enough that we can run

13 Feb 2023: The posterior predictive dis-tribution of the function value h = h(x) for an arbi-trary test input x is Gaussian with mean and variance. ... Consider the problem of predicting a function valueh(x) for an uncertain test input x N(µ, Σ), whereh GP with