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InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy [article]

Javier Cózar, Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa
2020 arXiv   pre-print
InferPy is a Python package for probabilistic modeling with deep neural networks.  ...  In particular, this package allows to define, learn and evaluate general hierarchical probabilistic models containing deep neural networks in a compact and simple way.  ...  Introduction Advances in variational methods [1] have made possible the development of a new formalism, namely deep probabilistic modeling [2] , which combines probabilistic models within deep neural  ... 
arXiv:1908.11161v4 fatcat:66mpykecezghhopawwhf5b2xne

A Survey of Uncertainty in Deep Neural Networks [article]

Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang (+2 others)
2022 arXiv   pre-print
The modeling of these uncertainties based on deterministic neural networks, Bayesian neural networks, ensemble of neural networks, and test-time data augmentation approaches is introduced and different  ...  Due to their increasing spread, confidence in neural network predictions became more and more important.  ...  Masegosa, “Inferpy: Probabilistic “Fast and scalable bayesian deep learning by weight-perturbation in modeling with tensorflow made easy,” Knowledge-Based Systems, vol.  ... 
arXiv:2107.03342v3 fatcat:cex5j3xq5fdijjdtdbt2ixralm