18,972 research outputs found
Calibrated Prediction Intervals for Neural Network Regressors
Ongoing developments in neural network models are continually advancing the
state of the art in terms of system accuracy. However, the predicted labels
should not be regarded as the only core output; also important is a
well-calibrated estimate of the prediction uncertainty. Such estimates and
their calibration are critical in many practical applications. Despite their
obvious aforementioned advantage in relation to accuracy, contemporary neural
networks can, generally, be regarded as poorly calibrated and as such do not
produce reliable output probability estimates. Further, while post-processing
calibration solutions can be found in the relevant literature, these tend to be
for systems performing classification. In this regard, we herein present two
novel methods for acquiring calibrated predictions intervals for neural network
regressors: empirical calibration and temperature scaling. In experiments using
different regression tasks from the audio and computer vision domains, we find
that both our proposed methods are indeed capable of producing calibrated
prediction intervals for neural network regressors with any desired confidence
level, a finding that is consistent across all datasets and neural network
architectures we experimented with. In addition, we derive an additional
practical recommendation for producing more accurate calibrated prediction
intervals. We release the source code implementing our proposed methods for
computing calibrated predicted intervals. The code for computing calibrated
predicted intervals is publicly available
Modeling the Temporal Nature of Human Behavior for Demographics Prediction
Mobile phone metadata is increasingly used for humanitarian purposes in
developing countries as traditional data is scarce. Basic demographic
information is however often absent from mobile phone datasets, limiting the
operational impact of the datasets. For these reasons, there has been a growing
interest in predicting demographic information from mobile phone metadata.
Previous work focused on creating increasingly advanced features to be modeled
with standard machine learning algorithms. We here instead model the raw mobile
phone metadata directly using deep learning, exploiting the temporal nature of
the patterns in the data. From high-level assumptions we design a data
representation and convolutional network architecture for modeling patterns
within a week. We then examine three strategies for aggregating patterns across
weeks and show that our method reaches state-of-the-art accuracy on both age
and gender prediction using only the temporal modality in mobile metadata. We
finally validate our method on low activity users and evaluate the modeling
assumptions.Comment: Accepted at ECML 2017. A previous version of this paper was titled
'Using Deep Learning to Predict Demographics from Mobile Phone Metadata' and
was accepted at the ICLR 2016 worksho
- …