A Fast Learning Algorithm for Deep Belief Nets
We show how to use “complementary priors” to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. U...
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We show how to use “complementary priors” to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. U...
Abstract Motivation: DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allo...
An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partia...
Abstract Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population‐based c...
Abstract Summary: RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies ...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. T...
Anticancer cytotoxic agents go through a process by which their antitumor activity-on the basis of the amount of tumor shrinkage they could generate-has been investigated. In th...
Overall health risks exceeded benefits from use of combined estrogen plus progestin for an average 5.2-year follow-up among healthy postmenopausal US women. All-cause mortality ...