Zobrazeno 1 - 7
of 7
pro vyhledávání: '"NABARRO, SETH"'
We propose an approach to do learning in Gaussian factor graphs. We treat all relevant quantities (inputs, outputs, parameters, latents) as random variables in a graphical model, and view both training and prediction as inference problems with differ
Externí odkaz:
http://arxiv.org/abs/2311.14649
Autor:
Nabarro, Seth, Ganev, Stoil, Garriga-Alonso, Adrià, Fortuin, Vincent, van der Wilk, Mark, Aitchison, Laurence
Bayesian neural networks that incorporate data augmentation implicitly use a ``randomly perturbed log-likelihood [which] does not have a clean interpretation as a valid likelihood function'' (Izmailov et al. 2021). Here, we provide several approaches
Externí odkaz:
http://arxiv.org/abs/2106.05586
Epidemiology models are central in understanding and controlling large scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian Computation (ABC) to fit their parameters to observations. ABC inferen
Externí odkaz:
http://arxiv.org/abs/2012.14332
Autor:
Laskin, Michael, Metz, Luke, Nabarro, Seth, Saroufim, Mark, Noune, Badreddine, Luschi, Carlo, Sohl-Dickstein, Jascha, Abbeel, Pieter
Deep learning models trained on large data sets have been widely successful in both vision and language domains. As state-of-the-art deep learning architectures have continued to grow in parameter count so have the compute budgets and times required
Externí odkaz:
http://arxiv.org/abs/2012.03837
Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner. Here we present a novel method for ambulance demand prediction using Gaussian process regression (GPR) in tim
Externí odkaz:
http://arxiv.org/abs/1806.10873
Akademický článek
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Publikováno v:
ACM Journal on Emerging Technologies in Computing Systems; Dec2021, Vol. 18 Issue 2, p1-24, 24p
