69,440 research outputs found
Predictive Uncertainty Estimation via Prior Networks
Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to distributional mismatch between the test and training data distributions. Different actions might be taken depending on the source of the uncertainty so it is important to be able to distinguish between them. Recently, baseline tasks and metrics have been defined and several practical methods to estimate uncertainty developed. These methods, however, attempt to model uncertainty due to distributional mismatch either implicitly through model uncertainty or as data uncertainty. This work proposes a new framework for modeling predictive uncertainty called Prior Networks (PNs) which explicitly models distributional uncertainty. PNs do this by parameterizing a prior distribution over predictive distributions. This work focuses on uncertainty for classification and evaluates PNs on the tasks of identifying out-of-distribution (OOD) samples and detecting misclassification on the MNIST dataset, where they are found to outperform previous methods. Experiments on synthetic and MNIST data show that unlike previous non-Bayesian methods PNs are able to distinguish between data and distributional uncertainty
Crowd Counting with Decomposed Uncertainty
Research in neural networks in the field of computer vision has achieved
remarkable accuracy for point estimation. However, the uncertainty in the
estimation is rarely addressed. Uncertainty quantification accompanied by point
estimation can lead to a more informed decision, and even improve the
prediction quality. In this work, we focus on uncertainty estimation in the
domain of crowd counting. With increasing occurrences of heavily crowded events
such as political rallies, protests, concerts, etc., automated crowd analysis
is becoming an increasingly crucial task. The stakes can be very high in many
of these real-world applications. We propose a scalable neural network
framework with quantification of decomposed uncertainty using a bootstrap
ensemble. We demonstrate that the proposed uncertainty quantification method
provides additional insight to the crowd counting problem and is simple to
implement. We also show that our proposed method exhibits the state of the art
performances in many benchmark crowd counting datasets.Comment: Accepted in AAAI 2020 (Main Technical Track
Controlling Risk of Web Question Answering
Web question answering (QA) has become an indispensable component in modern
search systems, which can significantly improve users' search experience by
providing a direct answer to users' information need. This could be achieved by
applying machine reading comprehension (MRC) models over the retrieved passages
to extract answers with respect to the search query. With the development of
deep learning techniques, state-of-the-art MRC performances have been achieved
by recent deep methods. However, existing studies on MRC seldom address the
predictive uncertainty issue, i.e., how likely the prediction of an MRC model
is wrong, leading to uncontrollable risks in real-world Web QA applications. In
this work, we first conduct an in-depth investigation over the risk of Web QA.
We then introduce a novel risk control framework, which consists of a qualify
model for uncertainty estimation using the probe idea, and a decision model for
selectively output. For evaluation, we introduce risk-related metrics, rather
than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA.
The empirical results over both the real-world Web QA dataset and the academic
MRC benchmark collection demonstrate the effectiveness of our approach.Comment: 42nd International ACM SIGIR Conference on Research and Development
in Information Retrieva
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