114,102 research outputs found
Insurability Challenges Under Uncertainty: An Attempt to Use the Artificial Neural Network for the Prediction of Losses from Natural Disasters
The main difficulty for natural disaster insurance derives from the uncertainty of an event’s damages. Insurers cannot precisely appreciate the weight of natural hazards because of risk dependences. Insurability under uncertainty first requires an accurate assessment of entire damages. Insured and insurers both win when premiums calculate risk properly. In such cases, coverage will be available and affordable. Using the artificial neural network – a technique rooted in artificial intelligence - insurers can predict annual natural disaster losses. There are many types of artificial neural network models. In this paper we use the multilayer perceptron neural network, the most accommodated to the prediction task. In fact, if we provide the natural disaster explanatory variables to the developed neural network, it calculates perfectly the potential annual losses for the studied country.Natural disaster losses, Insurability, Uncertainty, Multilayer perceptron neural network, Prediction.
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes
Stochastic variational inference for Bayesian deep neural network (DNN)
requires specifying priors and approximate posterior distributions over neural
network weights. Specifying meaningful weight priors is a challenging problem,
particularly for scaling variational inference to deeper architectures
involving high dimensional weight space. We propose MOdel Priors with Empirical
Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian
neural networks. We formulate a two-stage hierarchical modeling, first find the
maximum likelihood estimates of weights with DNN, and then set the weight
priors using empirical Bayes approach to infer the posterior with variational
inference. We empirically evaluate the proposed approach on real-world tasks
including image classification, video activity recognition and audio
classification with varying complex neural network architectures. We also
evaluate our proposed approach on diabetic retinopathy diagnosis task and
benchmark with the state-of-the-art Bayesian deep learning techniques. We
demonstrate MOPED method enables scalable variational inference and provides
reliable uncertainty quantification.Comment: To be published at AAAI 2020 conferenc
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness
Robust machine learning models with accurately calibrated uncertainties are
crucial for safety-critical applications. Probabilistic machine learning and
especially the Bayesian formalism provide a systematic framework to incorporate
robustness through the distributional estimates and reason about uncertainty.
Recent works have shown that approximate inference approaches that take the
weight space uncertainty of neural networks to generate ensemble prediction are
the state-of-the-art. However, architecture choices have mostly been ad hoc,
which essentially ignores the epistemic uncertainty from the architecture
space. To this end, we propose a Unified probabilistic architecture and weight
ensembling Neural Architecture Search (UraeNAS) that leverages advances in
probabilistic neural architecture search and approximate Bayesian inference to
generate ensembles form the joint distribution of neural network architectures
and weights. The proposed approach showed a significant improvement both with
in-distribution (0.86% in accuracy, 42% in ECE) CIFAR-10 and
out-of-distribution (2.43% in accuracy, 30% in ECE) CIFAR-10-C compared to the
baseline deterministic approach
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