Reducing the Dimensionality of Data with Neural Networks
High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors....
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Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressi...
The Evolution of Cooperation
Cooperation in organisms, whether bacteria or primates, has been a difficulty for evolutionary theory since Darwin. On the assumption that interactions between pairs of individu...
GRADE: an emerging consensus on rating quality of evidence and strength of recommendations
Guidelines are inconsistent in how they rate the quality of evidence and the strength of recommendations. This article explores the advantages of the GRADE system, which is incr...
Very high resolution interpolated climate surfaces for global land areas
We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The c...
Regularization and Variable Selection Via the Elastic Net
Summary We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the la...
Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biologi...
Increasing Returns and Long-Run Growth
This paper presents a fully specified model of long-run growth in which knowledge is assumed to be an input in production that has increasing marginal productivity. It is essent...