Minimax Bayesian Neural Networks [PDF]
EntropyRobustness is an important issue in deep learning, and Bayesian neural networks (BNNs) provide means of robustness analysis, while the minimax method is a conservative choice in the classical Bayesian field.
Junping Hong, Ercan Engin Kuruoglu
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Winsorization for Robust Bayesian Neural Networks [PDF]
Entropy, 2021 With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers.
Somya Sharma, Snigdhansu Chatterjee
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Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks [PDF]
Nature Communications, 2023 Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment.
Djohan Bonnet +12 more
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Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic. [PDF]
PLoS ONE, 2021 Recently, there has been much attention in the use of machine learning methods, particularly deep learning for stock price prediction. A major limitation of conventional deep learning is uncertainty quantification in predictions which affect investor ...
Rohitash Chandra, Yixuan He
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Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks [PDF]
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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Layer wise Scaled Gaussian Priors for Markov Chain Monte Carlo Sampled deep Bayesian neural networks [PDF]
Frontiers in Artificial IntelligencePrevious work has demonstrated that initialization is very important for both fitting a neural network by gradient descent methods, as well as for Variational inference of Bayesian neural networks.
Devesh Jawla, John Kelleher
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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 ...
Nam Nguyen, Kwang-Cheng Chen
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Approximate Bayesian neural networks in genomic prediction [PDF]
Genetics Selection Evolution, 2018 Background Genome-wide marker data are used both in phenotypic genome-wide association studies (GWAS) and genome-wide prediction (GWP). Typically, such studies include high-dimensional data with thousands to millions of single nucleotide polymorphisms ...
Patrik Waldmann
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Cox proportional hazards model with Bayesian neural network for survival prediction [PDF]
Scientific ReportsSurvival analysis plays a crucial aspect in medical research and other domains where understanding the time-to-events is paramount. In this study, we present a novel approach for estimating survival outcomes that combines Bayesian neural networks with ...
Fojan Faghiri, Akram Kohansal
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Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee [PDF]
Pesquisa Agropecuária Brasileira, 2017 : The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica).
Gabi Nunes Silva +9 more
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