18,819 research outputs found
Perturbation analysis for word-length optimization
Published versio
Optimal realizations of floating-point implemented digital controllers with finite word length considerations.
The closed-loop stability issue of finite word length (FWL) realizations is
investigated for digital controllers implemented in floating-point arithmetic.
Unlike the existing methods which only address the effect of the mantissa bits
in floating-point implementation to the sensitivity of closed-loop stability,
the sensitivity of closed-loop stability is analysed with respect to both the
mantissa and exponent bits of floating-point implementation. A computationally
tractable FWL closed-loop stability measure is then defined, and the method of
computing the value of this measure is given. The optimal controller realization
problem is posed as searching for a floating-point realization that maximizes
the proposed FWL closed-loop stability measure, and a numerical optimization
technique is adopted to solve for the resulting optimization problem. Simulation
results show that the proposed design procedure yields computationally efficient
controller realizations with enhanced FWL closed-loop stability performance
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
We apply convolutional neural networks (ConvNets) to the task of
distinguishing pathological from normal EEG recordings in the Temple University
Hospital EEG Abnormal Corpus. We use two basic, shallow and deep ConvNet
architectures recently shown to decode task-related information from EEG at
least as well as established algorithms designed for this purpose. In decoding
EEG pathology, both ConvNets reached substantially better accuracies (about 6%
better, ~85% vs. ~79%) than the only published result for this dataset, and
were still better when using only 1 minute of each recording for training and
only six seconds of each recording for testing. We used automated methods to
optimize architectural hyperparameters and found intriguingly different ConvNet
architectures, e.g., with max pooling as the only nonlinearity. Visualizations
of the ConvNet decoding behavior showed that they used spectral power changes
in the delta (0-4 Hz) and theta (4-8 Hz) frequency range, possibly alongside
other features, consistent with expectations derived from spectral analysis of
the EEG data and from the textual medical reports. Analysis of the textual
medical reports also highlighted the potential for accuracy increases by
integrating contextual information, such as the age of subjects. In summary,
the ConvNets and visualization techniques used in this study constitute a next
step towards clinically useful automated EEG diagnosis and establish a new
baseline for future work on this topic.Comment: Published at IEEE SPMB 2017 https://www.ieeespmb.org/2017
A Practical Algorithm for Topic Modeling with Provable Guarantees
Topic models provide a useful method for dimensionality reduction and
exploratory data analysis in large text corpora. Most approaches to topic model
inference have been based on a maximum likelihood objective. Efficient
algorithms exist that approximate this objective, but they have no provable
guarantees. Recently, algorithms have been introduced that provide provable
bounds, but these algorithms are not practical because they are inefficient and
not robust to violations of model assumptions. In this paper we present an
algorithm for topic model inference that is both provable and practical. The
algorithm produces results comparable to the best MCMC implementations while
running orders of magnitude faster.Comment: 26 page
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