In computer science, sparse conditional constant propagation (SCCP) is an optimization frequently applied in compilers after conversion to static single...

propagation known as sparse conditional constant propagation can more accurately propagate constants and simultaneously remove dead code. Constant folding is the...

System 7 networks. South Carolina College of Pharmacy. Sparse conditional constant propagation, an optimisation technique used in compilers. Sport Club...

partial-redundancy elimination, global value numbering, sparse conditional constant propagation, and scalar replacement of aggregates. Array dependence...

elimination cannot, and vice versa. Sparse conditional constant propagation Combines constant propagation, constant folding, and dead-code elimination...

propagation – precompute the potential ranges a calculation could be, allowing for the creation of branch predictions in advance Sparse conditional constant...

be to predict the likelihood of a homeowner defaulting on a mortgage. Conditional random fields, an extension of logistic regression to sequential data...

separately as was first shown in where it was called "the bunch-mode back-propagation algorithm". It may also result in smoother convergence, as the gradient...

delay relative to training-set loss; and the introduction of sparse autoencoders, a sparse dictionary learning method to extract interpretable features...

expectation Conditional independence Conditional probability Conditional probability distribution Conditional random field Conditional variance Conditionality principle...

processing) is a theory of brain function which postulates that the brain is constantly generating and updating a "mental model" of the environment. According...

layers, the number of pixels in the receptive field remains constant, but the field is more sparsely populated as its dimensions grow when combining the effect...

hidden units are activated (i.e. have a non-zero output). Better gradient propagation: fewer vanishing gradient problems compared to sigmoidal activation functions...

added to GPUs. Conditional writes could be performed using a properly crafted series of arithmetic/bit operations, but looping and conditional branching were...

Loss of significance Numerical error Numerical stability Error propagation: Propagation of uncertainty Residual (numerical analysis) Relative change and...

Another possibility is to integrate Fuzzy Rule Interpolation (FRI) and use sparse fuzzy rule-bases instead of discrete Q-tables or ANNs, which has the advantage...

convolutional variants, ssRBMs, deep coding networks, DBNs with sparse feature learning, RNNs, conditional DBNs, denoising autoencoders. This provides a better representation...

analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should...

learning by saying that whereas supervised learning intends to infer a conditional probability distribution conditioned on the label of input data; unsupervised...

ISBN 978-0-8308-5368-7. Sermon preached in June 1864 to the British Society for the Propagation of the Gospel among the Jews 'The Jew', July 1870, The Quarterly Journal...

being deported to Manila. The islands were fragmented and sparsely populated due to constant inter-kingdom wars and natural disasters (as the country is...

population genetics, ecology, epidemiology, systems biology, and in radio propagation. The first ABC-related ideas date back to the 1980s. Donald Rubin, when...

Anthony J.; Fallside, Frank (1987). The Utility Driven Dynamic Error Propagation Network. Technical Report CUED/F-INFENG/TR.1. Department of Engineering...

document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision...

Technical Information Division, 1989. Draper, David. "Assessment and propagation of model uncertainty." Journal of the Royal Statistical Society, Series...

contains relevant information. Real high-dimensional data is typically sparse, and tends to have relevant low dimensional features. One task of TDA is...