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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,
A gaussian mixture model was fitted to experimental data recorded under darkness by a camera for capturing astronomical images in order to model the distribution of hot pixels and
In the ordinal regression model, we trained with soft thresholds since we needed the model to be differentiable end to end. In the post-hoc model, we searched threshold space in
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
(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..
