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Bayesian Multi-Scale Optimistic Optimization
In this paper, we introduce a new technique for efficient global optimization that combines Gaussian process confidence bounds and treed simultaneous optimistic optimization to eliminate the need for auxiliary optimization of acquisition functions.
Predicting Parameters in Deep Learning
We demonstrate that there is significant redundancy in the parameterization of several deep learning models.
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