41,369 research outputs found
Accelerating sparse restricted Boltzmann machine training using non-Gaussianity measures
In recent years, sparse restricted Boltzmann machines have gained popularity as unsupervised feature extractors. Starting from the observation that their training process is biphasic, we investigate how it can be accelerated: by determining when it can be stopped based on the non-Gaussianity of the distribution of the model parameters, and by increasing the learning rate when the learnt filters have locked on to their preferred configurations. We evaluated our approach on the CIFAR-10, NORB and GTZAN datasets
Bayesian Semi-supervised Learning with Graph Gaussian Processes
We propose a data-efficient Gaussian process-based Bayesian approach to the
semi-supervised learning problem on graphs. The proposed model shows extremely
competitive performance when compared to the state-of-the-art graph neural
networks on semi-supervised learning benchmark experiments, and outperforms the
neural networks in active learning experiments where labels are scarce.
Furthermore, the model does not require a validation data set for early
stopping to control over-fitting. Our model can be viewed as an instance of
empirical distribution regression weighted locally by network connectivity. We
further motivate the intuitive construction of the model with a Bayesian linear
model interpretation where the node features are filtered by an operator
related to the graph Laplacian. The method can be easily implemented by
adapting off-the-shelf scalable variational inference algorithms for Gaussian
processes.Comment: To appear in NIPS 2018 Fixed an error in Figure 2. The previous arxiv
version contains two identical sub-figure
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