Zobrazeno 1 - 10
of 431
pro vyhledávání: '"Normalizing Flows"'
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 10, Iss 1, Pp 1-27 (2023)
Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining co
Externí odkaz:
https://doaj.org/article/2bcf55e60298452bb46b71fc20c9773d
Publikováno v:
Symmetry, Vol 16, Iss 8, p 942 (2024)
Normalizing flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions but also deliver density estimation by construction. We propose here an in-depth comparison of
Externí odkaz:
https://doaj.org/article/8dc6c942fd8c45bc8130309733a8ee46
Publikováno v:
Entropy, Vol 26, Iss 7, p 593 (2024)
We introduce NodeFlow, a flexible framework for probabilistic regression on tabular data that combines Neural Oblivious Decision Ensembles (NODEs) and Conditional Continuous Normalizing Flows (CNFs). It offers improved modeling capabilities for arbit
Externí odkaz:
https://doaj.org/article/3d7724cabe1f45f3ba2039c5e0c13468
Publikováno v:
Sensors, Vol 24, Iss 4, p 1213 (2024)
Electrolysis stands as a pivotal method for environmentally sustainable hydrogen production. However, the formation of gas bubbles during the electrolysis process poses significant challenges by impeding the electrochemical reactions, diminishing cel
Externí odkaz:
https://doaj.org/article/3c73b3ef946d4876952a75d344ba919f
Publikováno v:
Sensors, Vol 24, Iss 4, p 1248 (2024)
Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially
Externí odkaz:
https://doaj.org/article/1e0f819a0d6d46c58b7b716cc7615056
Autor:
Daniel Andrade
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 4, p 045066 (2024)
Variational inference with normalizing flows is an increasingly popular alternative to MCMC methods. In particular, normalizing flows based on affine coupling layers (Real NVPs) are frequently used due to their good empirical performance. In theory,
Externí odkaz:
https://doaj.org/article/c1b8fbdcd66c433387d43adb4fd17a89
Autor:
Bálint Máté, François Fleuret
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 4, p 045053 (2024)
We consider the problem of sampling lattice field configurations on a lattice from the Boltzmann distribution corresponding to some action. Since such densities arise as approximationw of an underlying functional density, we frame the task as an inst
Externí odkaz:
https://doaj.org/article/4f8ff28da1d64c65b639cc12c2d3c60a
Autor:
Bo Liang, Minghui Du, He Wang, Yuxiang Xu, Chang Liu, Xiaotong Wei, Peng Xu, Li-e Qiang, Ziren Luo
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 4, p 045040 (2024)
Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as laser interferometer space antenna, Taiji, and Tianqin. The fast and accurate parameter estimatio
Externí odkaz:
https://doaj.org/article/deff55d350cd436786009dc46ea69182
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035061 (2024)
Orbital-free density functional theory (OF-DFT) for real-space systems has historically depended on Lagrange optimization techniques, primarily due to the inability of previously proposed electron density approaches to ensure the normalization constr
Externí odkaz:
https://doaj.org/article/3f1a876b8d6f49eb80b387698342517e
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035007 (2024)
The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomen
Externí odkaz:
https://doaj.org/article/692eac720ab24c9fafe32cd06d999200