Results 21 to 30 of about 234,090 (282)
Nature Communications, 2022 Designing efficient Bayesian neural networks remains a challenge. Here, the authors use the cycle variation in the programming of the 2D memtransistors to achieve Gaussian random number generator-based synapses, and combine it with the complementary 2D ...
Amritanand Sebastian +6 more
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Quantum Bayesian Neural Networks
, 2021 17 pages, 11 ...
Berner, Noah +2 more
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Bayesian Quantum Neural Networks
IEEE Access, 2022 The astounding acceleration in Artificial Intelligence and Quantum Computing advances naturally gives rise to a line of research, which unrolls the potential advantages of quantum computing on classical Machine Learning tasks, known as Quantum Machine Learning or Quantum Machine Intelligence.
Nam Nguyen, Kwang-Cheng Chen
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Neural Network Parameterizations of Electromagnetic Nucleon Form Factors [PDF]
, 2010 The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties.
A Bodek +78 more
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Explaining Bayesian Neural Networks
, 2021 To advance the transparency of learning machines such as Deep Neural Networks (DNNs), the field of Explainable AI (XAI) was established to provide interpretations of DNNs' predictions. While different explanation techniques exist, a popular approach is given in the form of attribution maps, which illustrate, given a particular data point, the relevant ...
Bykov, Kirill +6 more
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Ensemble Kalman filter for neural network based one-shot inversion
, 2020 We study the use of novel techniques arising in machine learning for inverse problems. Our approach replaces the complex forward model by a neural network, which is trained simultaneously in a one-shot sense when estimating the unknown parameters from ...
Guth, Philipp A. +2 more
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Ship Target Identification via Bayesian-Transformer Neural Network
Journal of Marine Science and Engineering, 2022 Ship target identification is of great significance in both military and civilian fields. Many methods have been proposed to identify the targets using tracks information. However, most of existing studies can only identify two or three types of targets,
Zhan Kong +5 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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On the determination of probability density functions by using Neural Networks [PDF]
, 1998 It is well known that the output of a Neural Network trained to disentangle between two classes has a probabilistic interpretation in terms of the a-posteriori Bayesian probability, provided that a unary representation is taken for the output patterns ...
Aurelio Juste +11 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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