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pro vyhledávání: '"Dayta, Dominic B."'
Autor:
Dayta, Dominic B.
We introduce YOASOVI, an algorithm for performing fast, self-correcting stochastic optimization for Variational Inference (VI) on large Bayesian heirarchical models. To accomplish this, we take advantage of available information on the objective func
Externí odkaz:
http://arxiv.org/abs/2406.02838
Autor:
Dayta, Dominic B.
Black Box Variational Inference is a promising framework in a succession of recent efforts to make Variational Inference more ``black box". However, in basic version it either fails to converge due to instability or requires some fine-tuning of the u
Externí odkaz:
http://arxiv.org/abs/2405.05485
Autor:
Dayta, Dominic B., Barrios, Erniel B.
Legacy procedures for topic modelling have generally suffered problems of overfitting and a weakness towards reconstructing sparse topic structures. With motivation from a consumer-generated corpora, this paper proposes semiparametric topic model, a
Externí odkaz:
http://arxiv.org/abs/2107.10651