Results 31 to 40 of about 51,507 (261)
A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
CoRR, 2023 Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query. These objectives are typically represented by Gaussian process (GP) surrogate models which are easy to optimize and support exact inference.
Yucen Lily Li +2 more
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Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation
PRX Quantum, 2021 Quantum metrology promises unprecedented measurement precision but suffers in practice from the limited availability of resources such as the number of probes, their coherence time, or nonclassical quantum states.
Lukas J. Fiderer +2 more
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Towards Reliable Parameter Extraction in MEMS Final Module Testing Using Bayesian Inference
Sensors, 2022 In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years.
Monika E. Heringhaus +3 more
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Predictions of bitcoin prices through machine learning based frameworks [PDF]
PeerJ Computer Science, 2021 The high volatility of an asset in financial markets is commonly seen as a negative factor. However short-term trades may entail high profits if traders open and close the correct positions.
Luisanna Cocco +2 more
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Using topological data analysis for building Bayesan neural networks
Научно-технический вестник информационных технологий, механики и оптикиFor the first time, a simplified approach to constructing Bayesian neural networks is proposed, combining computational efficiency with the ability to analyze the learning process.
A. S. Vatian +4 more
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Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning
IJAIN (International Journal of Advances in Intelligent Informatics), 2021 The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels.
Wandercleiton Cardoso, Renzo di Felice
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Self-Compression in Bayesian Neural Networks [PDF]
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020 submitted to 2020 IEEE International Workshop on Machine Learning for Signal ...
Giuseppina Carannante +3 more
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IEEE AccessCausal inference is an important function of the nervous system. To explore causal inference, Bayesian inference performs as the possible framework, mapping neural implementation onto various cortical areas.
Weisi Liu, Xiaogang Pan
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All-Spin Bayesian Neural Networks [PDF]
IEEE Transactions on Electron Devices, 2020 Probabilistic machine learning enabled by the Bayesian formulation has recently gained significant attention in the domain of automated reasoning and decision-making. While impressive strides have been made recently to scale up the performance of deep Bayesian neural networks, they have been primarily standalone software efforts without any regard to ...
Kezhou Yang +3 more
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Journal of the Brazilian Computer Society, 1997 Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a solution to the problem of ...
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