Considering Our Patients and Tempering Terminology
نویسندگان
چکیده
منابع مشابه
Variational Tempering
Variational inference (VI) combined with data subsampling enables approximate posterior inference with large data sets for otherwise intractable models, but suffers from poor local optima. We first formulate a deterministic annealing approach for the generic class of conditionally conjugate exponential family models. This algorithm uses a temperature parameter that deterministically deforms the...
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Simulated tempering (ST) is an established Markov chain Monte Carlo (MCMC) method for sampling from a multimodal density π(θ). Typically, ST involves introducing an auxiliary variable k taking values in a finite subset of [0, 1] and indexing a set of tempered distributions, say πk(θ) ∝ π(θ) k. In this case, small values of k encourage better mixing, but samples from π are only obtained when the...
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Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning. Approximate maximum likelihood training of RBMs typically necessitates sampling from these models. In many training scenarios, computationally efficient Gibbs sampling procedures are crippled by poor mixing. In this work we propose a novel method of sampling from Boltzmann machines that demonstrate...
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ژورنال
عنوان ژورنال: Clinical Journal of the American Society of Nephrology
سال: 2020
ISSN: 1555-9041,1555-905X
DOI: 10.2215/cjn.07960520