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6 Nov 2019: 𝜃)𝑞𝜙(𝜃) ]. (2.24). 5More generally, the argument that follows holds for any family of distributions 𝑞𝜙(𝜃) where the entropy𝔼[ log 𝑞𝜙(𝜃)] is invariant w.r.t. ... the global reparameterisation trick (2.23).Alternatively,

30 Oct 2019: Forthe network in figure 3.6, F L = 2, NL = 4, and HL = 24 = 16. ... 24 Unconditional synthesis. baseline synthesis system. 2254 utterances are used for training, 70 for validation and 72for testing.

30 Oct 2019: the best MCR in Cambridge. I’ve absolutely loved 24 Parkside, everyone here had made. ... h, ,i (2.24). Where is a non-terminal symbol, while , 2 (X [ V) are a string of terminalsand non-terminals in the source and target languages respectively, where

18 Nov 2019: qφ (z) =. qφ (z|x)p(x)dx (2.24). does not match the prior p(z).

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.

30 Oct 2019: 24. 3.4 Contrastive divergence. 253.4.1 Persistent contrastive divergence. 25. 3.5 Learning using extended mean field approximation. ... Denote by:. θ = Ep(f(X)) =f(x)p(x)dx. 24 Learning of Boltzmann machine.

30 Oct 2019: 48. List of tables xvii. 5.24 celebA data: average ELBO over the validation set (10,000 samples). ... Unsupervised learning is a field of machine learning in which the machine attempts todiscover structure and patterns in a dataset ([24]).

6 Nov 2019: It is proved thatwith complex enough neural network we can regenerate any form of distribution from simplenormal distribution [24]. ... Same as standard VAE setting [24], Gaussinprior N(0,I) acts as a constrain on the hidden variable z.

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

30 Oct 2019: D, xı. )È6. (2.24). We will discuss the derivation and computation of the PES acquisition function in Chap-ter 3. ... n. (x), vn. (x)) where. µ. n. (x) = „(x)( ‡2I)1y (3.24)v.