Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation
Popular approaches for quantifying predictive uncertainty in deep neural networks often involve distributions over weights or multiple models, for instance via Markov Chain sampling, ensembling, or Monte Carlo dropout. These techniques usually incur overhead by having to train multiple model instances or do not produce very diverse predictions. This comprehensive and extensive survey aims to familiarize the reader with an alternative class of models based on the concept of Evidential Deep Learning: For unfamiliar data, they aim to admit "what they don't know", and fall back onto a prior belief. Furthermore, they allow uncertainty estimation in a single model and forward pass by parameterizing distributions over distributions. This survey recapitulates existing works, focusing on the implementation in a classification setting, before surveying the application of the same paradigm to regression. We also reflect on the strengths and weaknesses compared to other existing methods and provide the most fundamental derivations using a unified notation to aid future research.
Code (0)
등록된 구현이 없습니다.
Tasks
SurveyMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Survey on Evidential Deep Learning For Single-Pass Uncertainty Estimation
Popular approaches for quantifying predictive uncertainty in deep neural networks often involve a set of weights or models, for instance via ensembling or Monte Carlo Dropout. These techniques usually produce overhead by…
SurveyGeneralized Evidential Deep Learning: From a Bayesian Perspective
Evidential Deep Learning (EDL) has emerged as an efficient, sampling-free strategy for uncertainty estimation. A series of EDL variants have been proposed to address specific limitations of the original framework, achiev…
A Comprehensive Survey on Evidential Deep Learning and Its Applications
Reliable uncertainty estimation has become a crucial requirement for the industrial deployment of deep learning algorithms, particularly in high-risk applications such as autonomous driving and medical diagnosis. However…
Autonomous DrivingDeep LearningMedical DiagnosisSurveyA new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area
In this contribution, we predict the wellhead protection area (WHPA, target), the shape and extent of which is influenced by the distribution of hydraulic conductivity (K), from a small number of tracing experiments (pre…
Bayesian InferenceExperimental DesignMulti-target regressionEvidential Physics-Informed Neural Networks for Scientific Discovery
We present the fundamental theory and implementation guidelines underlying Evidential Physics-Informed Neural Network (E-PINN) -- a novel class of uncertainty-aware PINN. It leverages the marginal distribution loss funct…