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30 Oct 2019: blue=full GP, red=sparse approx.).(Left: 24 pseudo-data. Right: 20 pseudo data.). Despite a small change in the number of pseudo-data, a qualitativechange in the approximation is observed.

6 Nov 2019: 1 23 4. 24. 131 2 3 4. 1 23 4.

30 Oct 2019: 22. 4.2.1 Pathological behaviour. 24. 4.3 Conclusion. 24. 5 Initialisation Schemes 27. ... p(ŷ|x̂, D, α) =. p(ŷ|f, x̂)p(f |D, α)df (2.24). 1M. Mm=1.

6 Nov 2019: 24. 5.1 Optimised λ , where the rows are the tasks and the columns are the models. ... 24 Weighted Interpolation. the score decreases as the weights are changed to favour the health-only model.

30 Oct 2019: k((bt,at),(bt,at))aTt k̃t1(bt,at) > ν (2.24). where. k̃t1(bt,at) = [k((bt,at),(b̃0,ã0)),.,k((bt,at),(b̃m,ãm))]T. at = K̃1t1k̃t1(bt,at). ... 24 Methods. Figure 3.7. Architecture of the BCM for a set of six domains.

6 Nov 2019: 24. xii Table of contents. 3.4 Data augmentation by unconditional GANs array. ... Fig. 3.11 SNDCGAN Classifier: training curve. 24 Methodology. (a) Fake samples: 20 epoches (b) Real samples.

6 Nov 2019: This bound is substituted into Equation 26 and F(q) inEquation 24 is found. ... Type of Clothing VAE Bayesian GPLVMT-shirt 33 44Pullover 59 63Coat 60 66Shirt 75 73Bag 24 32.

6 Nov 2019: 213.2 Independent Compression. 24. 4.1 Experimental Setup. 254.2 Image Examples from the MNIST Database. ... soft-weight sharing[24], and modifies it with the aim of further improving compression.

6 Nov 2019: 23. 3.3.1 Covariances. 243.3.2 Cross covariances. 243.3.3 Eigenfunction based inducing points and the mean field approximation 24. ... Thefirst term in (2.24) can be thought of as an approximate marginal likelihood and the secondterm is a regularization

18 Nov 2019: 24. 4 Pre-trained Language Models 254.1 GPT. 254.2 Transformer XL. 264.3 BERT. ... Loss(θ ) = CE(θ )λ. 2N θθ 2 (3.12). 24 Neural Network Language Models.