In the pharmaceutical industry, there are a variety of organizational and process approaches to coding and classifying patient delta. In any pharmaceutical development structure, automated coding of patient clinical d...
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In the pharmaceutical industry, there are a variety of organizational and process approaches to coding and classifying patient delta. In any pharmaceutical development structure, automated coding of patient clinical data greatly facilitates data analysis by reducing the amount of time spent on coding review. This paper will describe the clinical data encoding system currently in use at Astra Pharmaceuticals, L.P., and will present a portrait of a successful model for an autoencoding algorithm program. Computer-assisted coding cannot entirely substitute for coding and data review by qualified medical personnel;however a volume data autoencoding application can significantly improve the quality, consistency, and pace of the data coding process, thereby allowing for more efficient analysis and reporting in the execution of a clinical trial.
A generative neural network model, constrained by non-face examples chosen by an iterative algorithm, is applied to fact: detection. To improve the generalization ability of the model, another constraint based on the ...
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A generative neural network model, constrained by non-face examples chosen by an iterative algorithm, is applied to fact: detection. To improve the generalization ability of the model, another constraint based on the minimum description length is added. This model is tested and compared with another state-of-the-art face detection system on a large image test set collected at CMU.
This paper describes two new methods for modeling the manifolds of digitized images of handwritten digits. The models allow a priori information about the structure of the manifolds to be combined with empirical data....
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This paper describes two new methods for modeling the manifolds of digitized images of handwritten digits. The models allow a priori information about the structure of the manifolds to be combined with empirical data. Accurate modeling of the manifolds allows digits to be discriminated using the relative probability densities under the alternative models. One of the methods is grounded in principal components analysis, the other in factor analysis. Both methods are based on locally linear low-dimensional approximations to the underlying data manifold. Links with other methods that model the manifold are discussed.
The minimum description length (MDL) principle can be used to train the hidden units of a neural network to extract a representation I-hat is cheap to describe but nonetheless allows the input to be reconstructed accu...
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The minimum description length (MDL) principle can be used to train the hidden units of a neural network to extract a representation I-hat is cheap to describe but nonetheless allows the input to be reconstructed accurately. We show how MDL can be used to develop highly redundant population codes. Each hidden unit has a location in a low-dimensional implicit space. If the hidden unit activities form a bump of a standard shape in this space, they can be cheaply encoded by the center of this bump. So the weights from the input units to the hidden units in an autoencoder are trained to make the activities form a standard bump. The coordinates of the hidden units in the implicit space are also learned, thus allowing flexibility, as the network develops a discontinuous topography when presented with different input classes.
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