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pro vyhledávání: '"Yokoi, Soma"'
Autor:
Yokoi, Soma, Sato, Issei
Sum-product networks (SPNs) are probabilistic models characterized by exact and fast evaluation of fundamental probabilistic operations. Its superior computational tractability has led to applications in many fields, such as machine learning with tim
Externí odkaz:
http://arxiv.org/abs/2406.12353
Autor:
Yokoi, Soma, Sato, Issei
The current interpretation of stochastic gradient descent (SGD) as a stochastic process lacks generality in that its numerical scheme restricts continuous-time dynamics as well as the loss function and the distribution of gradient noise. We introduce
Externí odkaz:
http://arxiv.org/abs/1911.09011
Stochastic gradient Langevin dynamics (SGLD) is a computationally efficient sampler for Bayesian posterior inference given a large scale dataset. Although SGLD is designed for unbounded random variables, many practical models incorporate variables wi
Externí odkaz:
http://arxiv.org/abs/1903.02750
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