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Statistical hypothesis testing - Wikipedia

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In this paper, we investigate how to diversify LVMs in the paradigm of Bayesian learning, which has advantages complementary to point estimation, such as alleviating overfitting via model averaging and quantifying uncertainty. University of Cambridge Paper Abstract We show how any binary pairwise model may be uprooted to a fully symmetric model, wherein original singleton potentials are transformed to potentials on edges to an added variable, and then rerooted to a new model on the. A Deep Learning Approach to Unsupervised Ensemble Learning. Uri Shaham Yale University, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, Yuval Kluger Paper Abstract We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble.

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On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models. Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization.

Directional hypothesis error risk
Though useful in controlling the variance of the estimate, such models are often too restrictive in practical settings. Between non-additive models which often have large variance and first order additive models which have large bias, there has been little work to exploit the.
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