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An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. [1] It is part of the families of probabilistic graphical models and variational Bayesian methods .
ELKI is an open-source Java data mining toolkit that contains several anomaly detection algorithms, as well as index acceleration for them. PyOD is an open-source Python library developed specifically for anomaly detection. [56] scikit-learn is an open-source Python library that contains some algorithms for unsupervised anomaly detection.
Besides some static code analysis, it can be used to show violations of a configured coding standard. Duplicate code detection was removed [13] from Checkstyle. Eclipse: 2017-06-28 Yes; EPL: No Cross-platform IDE with own set of several hundred code inspections available for analyzing code on-the-fly in the editor and bulk analysis of the whole ...
According to [8] the first project that paved the way to a generic CEP language and execution model was the Rapide project in Stanford University, directed by David Luckham. In parallel there have been two other research projects: Infospheres in California Institute of Technology , directed by K. Mani Chandy , and Apama in University of ...
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]