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Found 6 papers, 2 papers with code
OpenIPDM: A Probabilistic Framework for Estimating the Deterioration and Effect of Interventions on Bridges
This paper describes OpenIPDM software for modelling the deterioration process of infrastructures using network-scale visual inspection data.
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
With few exceptions, neural networks have been relying on backpropagation and gradient descent as the inference engine in order to learn the model parameters, because the closed-form Bayesian inference for neural networks has been considered to be intractable.
Analytically Tractable Bayesian Deep Q-Learning
In this paper, we present how we can adapt the temporal difference Q-learning framework to make it compatible with the tractable approximate Gaussian inference (TAGI), which allows learning the parameters of a neural network using a closed-form analytical method.
Analytically Tractable Inference in Deep Neural Networks
Since its inception, deep learning has been overwhelmingly reliant on backpropagation and gradient-based optimization algorithms in order to learn weight and bias parameter values.
Tractable Approximate Gaussian Inference for Bayesian Neural Networks
In this paper, we propose an analytical method for performing tractable approximate Gaussian inference (TAGI) in Bayesian neural networks.
The Nataf-Beta Random Field Classifier: An Extension of the Beta Conjugate Prior to Classification Problems
This paper presents the Nataf-Beta Random Field Classifier, a discriminative approach that extends the applicability of the Beta conjugate prior to classification problems.
