Probable networks and plausible predictions — a review of practical Bayesian methods for supervised neural networks

Learning representations by back-propagating errors

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The Theory of Probability

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Statistical Decision Theory and Bayesian Analysis

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Bayesian Interpolation

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Bayesian Learning for Neural Networks

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Bayesian Inference in Statistical Analysis.

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A practical Bayesian framework for backpropagation networks

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Stastical Decision Theory and Bayesian Analysis.

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An adaptive manifold- and discrete empirical interpolation method-based reduced order model for nonlinear solids

El-Hajj, Z., & Matouš, K. (2025) · Journal of Computational Physics · doi:10.1016/j.jcp.2025.114069 · Cited by 2

SWAP-NODE: A regularization approach for deep convolutional neural networks

Yamashita, T., Tanaka, M., Yamauchi, Y., & Fujiyoshi, H. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351247 · Cited by 2

Goal Detection from Natural Language Queries

He, Y. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13881-2_16 · Cited by 2

Bayesian Fuzzy Clustering of Colored Graphs

Theis, F. J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28551-6_65 · Cited by 2

Neural Network Modeling for the Prediction of Texture Evolution of Hot Deformed Aluminum Alloys

Barat, P., & Withers, P. J. (2003) · Journal of Materials Engineering and Performance · doi:10.1361/105994903322692402 · Cited by 2

Modelling by artificial neural network of high temperature fatigue life of oxide dispersion strengthened nickel-based superalloy PM 1000

Kovan, V., Hammer, J., Mai, R., & Yüksel, M. (2008) · Materials at High Temperatures · doi:10.1179/096034008x331229 · Cited by 2

Comparative study on local and global strategies for confidence estimation in neural networks and extensions to improve their predictive power

Rodrigues, A. C., Neves, C. A. M. D. S., & Roisenberg, M. (2012) · Neural Computing and Applications · doi:10.1007/s00521-012-1051-x · Cited by 2

Evaluation of Imputation Methods in Ovarian Tumor Diagnostic Models Using Generalized Linear Models and Support Vector Machines

Dimou, I. N., CALSTER, B. V., Huffel, S. V., Timmerman, D., & Zervakis, M. (2009) · Medical Decision Making · doi:10.1177/0272989x09340579 · Cited by 2

A Bayesian Approach for the Estimation of AR Coefficients from Noisy Biomedical Data

Oikonomou, V. P., & Fotiadis, D. I. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4353027 · Cited by 2

Tackling Flow Stress of Zirconium Alloys

Das, A. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09451-z · Cited by 2

Variational learning of finite shifted scaled Dirichlet mixture models

Arjmandiasl, Z., Manouchehri, N., Bouguila, N., & Bentahar, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822314-7.00012-2 · Cited by 2

Hierarchical identification of nonlinear hybrid systems in a Bayesian framework

Madary, A., Momeni, H., Abate, A., & Larsen, K. G. (2022) · Information and Computation · doi:10.1016/j.ic.2022.104947 · Cited by 2

Variational Neural Networks implementation in Pytorch and JAX

Oleksiienko, I., Tran, D. T., & Iosifidis, A. (2022) · Software Impacts · doi:10.1016/j.simpa.2022.100431 · Cited by 2

Improving generalization performance of bagging ensemble via Bayesian approach

Kurogi, S., & Harashima, K. (2009) · IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA) · doi:10.1109/cira.2009.5423234 · Cited by 2

Segmenting the e-Commerce Market Using the Generative Topographic Mapping

Vellido, A., Lisboa, P. J. G., & Meehan, K. (2000) · Lecture notes in computer science · doi:10.1007/10720076_43 · Cited by 2

Spatio-Temporal Mixed Membership Models for Criminal Activity

Virtanen, S., & Girolami, M. (2021) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12642 · Cited by 2

Soft-Sensor Method Based on Least Square Support Vector Machines Within Bayesian Evidence Framework

Wang, W., Wang, T., Wei, H., & Zhao, H. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72395-0_67 · Cited by 2

Characterizing and Segmenting the Online Customer Market Using Neural Networks

Vellido, A., Lisboa, P. J. G., & Meehan, K. (2002) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1779-9_7 · Cited by 2

Asymptotic theory of empirical and variational Bayes learning

中島, 伸. (2006) · Cited by 2

Bayesian Modeling and Adaptive Monte Carlo with Geophysics Applications

Wang, J. (2013) · DukeSpace (Duke University) · Cited by 2

Understanding Deep Architectures by Interpretable Visual SummariesPreprint

Carletti, M., Godi, M., Aghaei, M., & Cristani, M. (2018) · arXiv (Cornell University) · Cited by 2

Bayesian Solutions to Multi-model Inferential Sensing Problems

Khatibisepehr, S. (2013) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3v698p5s · Cited by 2

Prior Activation Distribution (PAD): A Versatile Representation to Utilize DNN Hidden UnitsPreprint

Meegahapola, L., Subramaniam, V., Kaplan, L., & Misra, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02711 · Cited by 4

Group Pruning using a Bounded-Lp norm for Group Gating and RegularizationPreprint

Mummadi, C. K., Genewein, T., Zhang, D., Brox, T., & Fischer, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03463 · Cited by 3

Disentangling the Gauss-Newton Method and Approximate Inference for Neural NetworksPreprint

Immer, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.11994 · Cited by 4

Seeking New Physics in Cosmology with Bayesian Neural Networks I: Dark Energy and Modified GravityPreprint

Mancarella, M., Kennedy, J., Bose, B., & Lombriser, L. (2020) · arXiv (Cornell University) · Cited by 2

SkiNet: A Deep Learning Solution for Skin Lesion Diagnosis with Uncertainty Estimation and ExplainabilityPreprint

Singh, R. K., Gorantla, R., Allada, S. G., & Narra, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15049 · Cited by 2

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Quo vadis, Bayesian identification?

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Simultaneous Feature and Expert Selection within Mixture of ExpertsPreprint

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Use of Artificial Intelligence and Machine Learning algorithms to predict programming levels in Cochlear Implant patients

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A Contemporary Overview of Probabilistic Latent Variable ModelsPreprint

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Self-organizing maps and generalization: an algorithmic description of Numerosity and Variability EffectsPreprint

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The unified maximum a posteriori (MAP) framework for neuronal system identificationPreprint

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Bayesian inference over model-spaces increases the accuracy of model comparison and allows formal testing of hypotheses about model distributions in experimental populationsPreprint

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A Classification Scheme for Inverse Design of Molecules: from Targeted Electronic Properties to AtomicityPreprint

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Bayesian Prior Networks with PAC TrainingPreprint

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Scalable Group Level Probabilistic Sparse Factor AnalysisPreprint

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An Automatic Relevance Determination Prior Bayesian Neural Network for Controlled Variable SelectionPreprint

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Probing as Quantifying the Inductive Bias of Pre-trained Representations.Preprint

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Selección de dimensionalidad en análisis de componentes principales utilizando modelos bayesianos

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Electronic commerce software agents: the featured-based filtering approach

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Infinite Independent Components Analysis

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Neuro Computing for Micro Structural Analysis

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Mixed-resolution modeling of perceptions in the joint warfare system

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Bayesian Methods for Backpropagation Networks

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