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18 Nov 2019:  (2.24)where 0 = 01T1 is the zero matrix and > 0 = > 0T1T1 is the non zero matrix.To solve these numerical problems, we have rearranged the positions of zero elements ... 24 Algorithm Design. Algorithm 1 Probabilistic Line Search1: Input:

6 Nov 2019: Figure Credit:Gregor et al. (2015). 24. 5.1 Random selection of generated molecules for model variants trained on theChEMBL dataset.

6 Nov 2019: 24. 4.5 Reading from the memory module using a soft way (left) and a hard way(right). ... In [24], a memory module is used to store both attention vectors and hiddenstate vectors.

6 Nov 2019: 202.5.1 Stable GAN training. 222.5.2 infoGAN. 23. 2.6 Related Work. 24. ... 24. is paired with an expanding path, resulting in a U shaped architecture.

6 Nov 2019: Hassibi et al [24] formulated the problem of deciding which weight to prune as anconstrained optimization problem, and where they used the inverse of the full Hessianmatrix. ... 24. surface element of the hypersphere;. Z =. 0. S|g|1(r)erb dr (4.12).

6 Nov 2019: 24. Table of contents vii. 2.5.2 Constrained Training. 252.5.3 Post-Processing. 25. ... 2.23)= 0.05 (2.24).

6 Nov 2019: 24. 5.2.1 MNIST. 245.2.2 CALTECH101. 245.2.3 SVHN. 25. 5.3 Processing for Distribution Type.

18 Nov 2019: A(D) = iI. f (xi) σ ηηη (2.24). where ηi N(0,1) for i = 1,.,n and I is a subset of indices in which each index is chosenindependently ... 24 Experiments and Results. Exponential family distributions take the following form:.

30 Oct 2019: 2.3.2 Neural Programmer-Interpreters. Neural Programmer-Interpreter (NPI) [24] is an architecture that learns to represent andexecute programs.

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.