Results 31 to 40 of about 549,946 (276)
Deep generative model for probabilistic wind speed and wind power estimation at a wind farm
Energy Science & Engineering, 2022 This work introduces a novel method to generate probabilistic hub‐height wind speed forecasts aimed at power output prediction. We employ state‐of‐the‐art convolutional variational autoencoders (CVAEs) trained with historical wind speed observations ...
Andrés A. Salazar +3 more
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Generalized generalized spin models (four-weight spin models) [PDF]
Pacific Journal of Mathematics, 1995 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bannai, Eiichi, Bannai, Etsuko
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Generalized dielectric breakdown model [PDF]
Physical Review B, 1999 Submitted to ...
R Cafiero +4 more
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Future of the Artificial Intelligence: Object of Law or Legal Personality?
Journal of Digital Technologies and Law, 2023 Objective: to reveal the problems associated with legal regulation of public relations, in which artificial intelligence systems are used, and to rationally comprehend the possibility of endowing such systems with a legal subject status, which is being ...
I. A. Filipova, V. D. Koroteev
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Journal of the Royal Statistical Society. Series A (General), 1972 The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.
Nelder, J. A., Wedderburn, R. W. M.
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The generic model of general relativity [PDF]
Arabian Journal of Mathematics, 2019 55 pages, no ...
Tsamparlis, M., Paliathanasis, A.
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NatureAbstract Generative models cover various application areas, including image and video synthesis, natural language processing and molecular design, among many others1–11. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge12–14.
Shiqi Chen +4 more
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Journal of Advances in Modeling Earth Systems, 2023 Subgrid parameterizations of mesoscale eddies continue to be in demand for climate simulations. These subgrid parameterizations can be powerfully designed using physics and/or data‐driven methods, with uncertainty quantification.
Pavel Perezhogin +2 more
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Learning Latent Representations for Speech Generation and Transformation
, 2017 An ability to model a generative process and learn a latent representation for speech in an unsupervised fashion will be crucial to process vast quantities of unlabelled speech data.
Glass, James, Hsu, Wei-Ning, Zhang, Yu
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