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A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization
We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples.
Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction
This sequential model achieves 70. 3% Q8 accuracy on CB513 with a single model; an ensemble of these models produces 71. 4% Q8 accuracy on the same test set, improving upon the previous overall state of the art for the eight-class secondary structure problem.
Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning
We first train a series of deep neural networks to predict eight-class secondary structure labels given a protein's amino acid sequence information and find that using recent methods for regularization, such as dropout and weight-norm constraining, leads to measurable gains in accuracy.
Protein Secondary Structure Prediction
Protein Structure Prediction
