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Our contributions include i) an experimental study of dif- ferent CNN-based architectures of deep neural networks for Othello; ii) state-of-the-art move prediction accuracy on
b) We now compare GPLVM+SVM and GPDNN, e.g. the latent space classifier and its corresponding LSAN. The results are shown in Figure 4b. We observe that for all datasets except Spam,
This study presents a novel end-to-end architecture that learns hierarchical representations from raw EEG data using fully convolutional deep neural networks for the task
Risk bounds for the LSE in other shape-restricted regression problems In this section we consider the problems of convex regression (Example 1.3), isotonic regres- sion on a
1) In order that any algorithm works for the semi-supervised classification problem the initial training sample D n (whose size does not need to tend to infinity) must be well
In doing so, we introduce the basic meaning and relevance of the judgment to regard a sequence of quantities exchangeably, and we show how the fundamental theorem of probability can
This article presents a rigorous analysis for efficient statistically accurate algorithms for solving the Fokker-Planck equations associated with high-dimensional nonlinear
(c) Using Log-rank splitting criteria described in previous section, a node is split using the single predictor that maximizes the survival differences between daughter nodes..
