
Bayesian Neural Networks: Essentials
Bayesian neural networks utilize probabilistic layers that capture uncer...
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Probabilistic Models with Deep Neural Networks
Recent advances in statistical inference have significantly expanded the...
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InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy
InferPy is a Python package for probabilistic modeling with deep neural ...
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Epistemic Neural Networks
We introduce the epistemic neural network (ENN) as an interface for unce...
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Neurosymbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels
We present a probabilistic programmed deep kernel learning approach to p...
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Probabilistic Deep Learning using Random SumProduct Networks
Probabilistic deep learning currently receives an increased interest, as...
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Learning Probabilistic Programs Using Backpropagation
Probabilistic modeling enables combining domain knowledge with learning ...
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Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models
Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty. It is based on the use of probabilistic models and deep neural networks. We distinguish two approaches to probabilistic deep learning: probabilistic neural networks and deep probabilistic models. The former employs deep neural networks that utilize probabilistic layers which can represent and process uncertainty; the latter uses probabilistic models that incorporate deep neural network components which capture complex nonlinear stochastic relationships between the random variables. We discuss some major examples of each approach including Bayesian neural networks and mixture density networks (for probabilistic neural networks), and variational autoencoders, deep Gaussian processes and deep mixed effects models (for deep probabilistic models). TensorFlow Probability is a library for probabilistic modeling and inference which can be used for both approaches of probabilistic deep learning. We include its code examples for illustration.
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