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pro vyhledávání: '"Moore, Dave"'
Autor:
Alam, Sadaf R., Woods, Christopher, Williams, Matt, Moore, Dave, Prior, Isaac, Williams, Ethan, Yang-Turner, Fan, Pryor, Matt, Livenson, Ilja
Scientific workflows have become highly heterogenous, leveraging distributed facilities such as High Performance Computing (HPC), Artificial Intelligence (AI), Machine Learning (ML), scientific instruments (data-driven pipelines) and edge computing.
Externí odkaz:
http://arxiv.org/abs/2410.18411
Publikováno v:
International Journal of Construction Supply Chain Management, Vol 2, Iss 1, Pp 46-54 (2012)
This paper is the result of a desire to include social factors alongside environmental and economic considerations in Life Cycle Assessment studies for the construction sector. We describe a specific search for a method to include injurious impact fo
Externí odkaz:
https://doaj.org/article/ec71afc603f94180a025a177c25876ea
Normalizing flows have shown great success as general-purpose density estimators. However, many real world applications require the use of domain-specific knowledge, which normalizing flows cannot readily incorporate. We propose embedded-model flows
Externí odkaz:
http://arxiv.org/abs/2110.06021
Publikováno v:
International Journal of Lean Six Sigma, 2023, Vol. 14, Issue 7, pp. 1655-1714.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJLSS-10-2022-0218
Autor:
Lao, Junpeng, Suter, Christopher, Langmore, Ian, Chimisov, Cyril, Saxena, Ashish, Sountsov, Pavel, Moore, Dave, Saurous, Rif A., Hoffman, Matthew D., Dillon, Joshua V.
Markov chain Monte Carlo (MCMC) is widely regarded as one of the most important algorithms of the 20th century. Its guarantees of asymptotic convergence, stability, and estimator-variance bounds using only unnormalized probability functions make it i
Externí odkaz:
http://arxiv.org/abs/2002.01184
Autor:
Ambrogioni, Luca, Lin, Kate, Fertig, Emily, Vikram, Sharad, Hinne, Max, Moore, Dave, van Gerven, Marcel
Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach depends on the choice of an appropriate variational family. Here, we i
Externí odkaz:
http://arxiv.org/abs/2002.00643
A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistributions, a family of declarative representations of directed graphica
Externí odkaz:
http://arxiv.org/abs/2001.11819
Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text. In this paper, we show that fine-tuning BERT on legal documents similarly provides valuable improve
Externí odkaz:
http://arxiv.org/abs/1911.00473
Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow modellers to directly reason about the processes generating data. However,
Externí odkaz:
http://arxiv.org/abs/1906.03028
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