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On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks
Multiple techniques for producing calibrated predictive probabilities using deep neural networks in supervised learning settings have emerged that leverage approaches to ensemble diverse solutions discovered during cyclic training or training from multiple random starting points (deep ensembles).
Randomized Approximation of the Gram Matrix: Exact Computation and Probabilistic Bounds
Given a real matrix A with n columns, the problem is to approximate the Gram product AA^T by c << n weighted outer products of columns of A.
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