37,007 research outputs found
A Neural Network model with Bidirectional Whitening
We present here a new model and algorithm which performs an efficient Natural
gradient descent for Multilayer Perceptrons. Natural gradient descent was
originally proposed from a point of view of information geometry, and it
performs the steepest descent updates on manifolds in a Riemannian space. In
particular, we extend an approach taken by the "Whitened neural networks"
model. We make the whitening process not only in feed-forward direction as in
the original model, but also in the back-propagation phase. Its efficacy is
shown by an application of this "Bidirectional whitened neural networks" model
to a handwritten character recognition data (MNIST data).Comment: 16page
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Predicting protein properties such as solvent accessibility and secondary
structure from its primary amino acid sequence is an important task in
bioinformatics. Recently, a few deep learning models have surpassed the
traditional window based multilayer perceptron. Taking inspiration from the
image classification domain we propose a deep convolutional neural network
architecture, MUST-CNN, to predict protein properties. This architecture uses a
novel multilayer shift-and-stitch (MUST) technique to generate fully dense
per-position predictions on protein sequences. Our model is significantly
simpler than the state-of-the-art, yet achieves better results. By combining
MUST and the efficient convolution operation, we can consider far more
parameters while retaining very fast prediction speeds. We beat the
state-of-the-art performance on two large protein property prediction datasets.Comment: 8 pages ; 3 figures ; deep learning based sequence-sequence
prediction. in AAAI 201
Artificial Neural Network to predict mean monthly total ozone in Arosa, Switzerland
Present study deals with the mean monthly total ozone time series over Arosa,
Switzerland. The study period is 1932-1971. First of all, the total ozone time
series has been identified as a complex system and then Artificial Neural
Networks models in the form of Multilayer Perceptron with back propagation
learning have been developed. The models are Single-hidden-layer and
Two-hidden-layer Perceptrons with sigmoid activation function. After sequential
learning with learning rate 0.9 the peak total ozone period (February-May)
concentrations of mean monthly total ozone have been predicted by the two
neural net models. After training and validation, both of the models are found
skillful. But, Two-hidden-layer Perceptron is found to be more adroit in
predicting the mean monthly total ozone concentrations over the aforesaid
period.Comment: 22 pages, 14 figure
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