3,067 research outputs found
Comparison of echo state network output layer classification methods on noisy data
Echo state networks are a recently developed type of recurrent neural network
where the internal layer is fixed with random weights, and only the output
layer is trained on specific data. Echo state networks are increasingly being
used to process spatiotemporal data in real-world settings, including speech
recognition, event detection, and robot control. A strength of echo state
networks is the simple method used to train the output layer - typically a
collection of linear readout weights found using a least squares approach.
Although straightforward to train and having a low computational cost to use,
this method may not yield acceptable accuracy performance on noisy data.
This study compares the performance of three echo state network output layer
methods to perform classification on noisy data: using trained linear weights,
using sparse trained linear weights, and using trained low-rank approximations
of reservoir states. The methods are investigated experimentally on both
synthetic and natural datasets. The experiments suggest that using regularized
least squares to train linear output weights is superior on data with low
noise, but using the low-rank approximations may significantly improve accuracy
on datasets contaminated with higher noise levels.Comment: 8 pages. International Joint Conference on Neural Networks (IJCNN
2017
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
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