Sequence to Sequence Learning with Neural Networks
Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training set...
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Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training set...
Astroplan is an observation planning package for astronomers. It is an astropy-affiliated package which began as a Google Summer of Code project. Astroplan facilitates convenien...
Olson develops a theory of group and organizational behavior that cuts across disciplinary lines and illustrates the theory with empirical and historical studies of particular o...
How should we understand why firms exist? A prevailing view has been that they serve to keep in check the transaction costs arising from the self-interested motivations of indiv...
First published in 1967, Professor Batchelor's classic text on fluid dynamics is still one of the foremost texts in the subject. The careful presentation of the underlying theor...
Both in science and in practical affairs we reason by combining facts only inconclusively supported by evidence. Building on an abstract understanding of this process of combina...
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in ...
Abstract Given C samples, with n i observations in the ith sample, a test of the hypothesis that the samples are from the same population may be made by ranking the observations...
Written in an outstandingly clear and lively style, this 1969 book provokes its readers to rethink issues they may have regarded as long since settled.
As vertebrate genome sequences near completion and research refocuses to their analysis, the issue of effective genome annotation display becomes critical. A mature web tool for...