Titlebook: Artificial Neural Networks; Methods and Applicat Petia Koprinkova-Hristova,Valeri Mladenov,Nikola K Conference proceedings 2015 Springer In

SMART

Feministische Methodologien und Methodenlly that it is difficult to train a DBM with approximate maximum-likelihood learning using the stochastic gradient unlike its simpler special case, restricted Boltzmann machine (RBM). In this paper, we propose a novel pretraining algorithm that consists of two stages; obtaining approximate posterior

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,Einführung: ‚Staat‘ und ‚Geschlecht‘,k architectures, different derivatives calculation and optimization methods and analyze their advantages and disadvantages. We propose a novel method for training feedforward neural networks with tapped delay lines for better multi-step-ahead predictions. Special mini-batch calculations of derivativ

CANON

https://doi.org/10.1007/978-3-663-10057-7ts with a richer environment, compactly described by the notion of constraint. Variational calculus is exploited to derive general representer theorems that give a description of the structure of the solution to the learning problem. It is shown that such solution can be represented in terms of ., w

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https://doi.org/10.1057/9780230592247 lot of attention lately. The basic method from this field, Policy Gradients with Parameter-based Exploration, uses two samples that are symmetric around the current hypothesis to circumvent misleading reward in . reward distributed problems gathered with the usual baseline approach. The exploration

Formidable

COMMA

Inexorable

Cytology

https://doi.org/10.1057/9780230592247ultidimensional features. When used as the only regularizer, GTV can be applied jointly with iterative convex optimization algorithms such as FISTA. This requires to compute its proximal operator which we derive using a dual formulation. GTV can also be combined with a Group Lasso (GL) regularizer,

Root494

capillaries