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The Infinite Hidden Markov Model Matthew J. Beal Zoubin ...
https://mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf27 Jan 2023: We propose estimating the likelihood of a test sequence given a learned model using particlefiltering. ... 6Different particle initialisations apply if we do not assume that the test sequence immediatelyfollows the training sequence. -
The Supervised IBP: Neighbourhood PreservingInfinite Latent Feature…
https://mlg.eng.cam.ac.uk/pub/pdf/QuaShaKnoGha13.pdf13 Feb 2023: the in-ferred test latent variable z with respect to the train-ing latent variables Z. ... boldface is significant using a one-sided paired t-test with 95% confidence. -
1 Learning the Structure of Deep Sparse Graphical Models ...
https://mlg.eng.cam.ac.uk/pub/pdf/AdaWalGha10.pdf13 Feb 2023: c) (d)Figure 4: Olivetti faces a) Test images on the left, withreconstructed bottom halves on the right. ... Fig 4ashows six bottom-half test set reconstructions on theright, compared to the ground truth on the left. -
Archipelago: Nonparametric Bayesian Semi-Supervised Learning Ryan…
https://mlg.eng.cam.ac.uk/pub/pdf/AdaGha09.pdf13 Feb 2023: In almost all of our tests, Archipelagohad lower test classification error than the NCNM. ... Itimproves over mixture-based Bayesian approaches toSSL while still modeling complex density functions.In empirical tests, our model compares favorably -
Gibbs sampling (an MCMC method) and relations to EM
https://mlg.eng.cam.ac.uk/zoubin/SALD/week7at.pdf27 Jan 2023: Uniform U(0, θ] distribution, θ > 0. We select at random m n disks, having a common θ for failure We select n of these (at random) and test them until -
chu05a.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/ChuGha05a.pdf13 Feb 2023: 0.23370.0072. Table 2: Test results of the three algorithms using a Gaussian kernel. ... Figure 4 presents the test results of the three algorithms for different numbers ofselected genes. -
Dependent Indian Buffet Processes Sinead Williamson Peter Orbanz…
https://mlg.eng.cam.ac.uk/pub/pdf/WilOrbGha10.pdf13 Feb 2023: The data was randomly split into atraining set of 130 countries, and a test set of 14 coun-tries. ... For each test country, one randomly selectedindicator was observed, and the remainder held outfor prediction. -
Learning to Control a Low-Cost Manipulator usingData-Efficient…
https://mlg.eng.cam.ac.uk/pub/pdf/DeiRasFox11.pdf13 Feb 2023: Althoughdeposit failure feedback was not available to the learner, thedeposit success is good across 10 test trials and four differenttraining setups. ... Second, Tab. III reports the block-deposit success rates for10 test runs (and four different -
Probabilistic Modelling, Machine Learning,and the Information…
https://mlg.eng.cam.ac.uk/zoubin/talks/mit12csail.pdf27 Jan 2023: Some Comparisons. Table 1: Test errors and predictive accuracy (smaller is better) for the GP classifier, the supportvector machine, the informative vector machine, and the sparse pseudo-inputGP classifier. ... Data set GPC SVM IVM SPGPC. name train:test -
Approximate inference for the loss-calibrated Bayesian
https://mlg.eng.cam.ac.uk/pub/pdf/LacHusGha11.pdf13 Feb 2023: We also assume. the transductive scenario where we are given a test setS of S points {xs}Ss=1, i.e. ... of the shift between the test and trainingdistributions (columns) and the asymmetry of loss (rows).
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