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For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
C++, Java: Java, Scala, Clojure, Python , Kotlin: Yes No [8] Yes [9] [10] No Computational Graph Yes [11] Yes Yes Yes Yes [12] Yes Dlib: Davis King 2002 Boost Software License: Yes Cross-platform: C++: C++, Python: Yes No Yes No Yes Yes No Yes Yes Yes Yes Flux: Mike Innes 2017 MIT license: Yes Linux, MacOS, Windows (Cross-platform) Julia: Julia ...
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.
Frames are the primary data structure used in artificial intelligence frame languages; they are stored as ontologies of sets. Frames are also an extensive part of knowledge representation and reasoning schemes. They were originally derived from semantic networks and are therefore part of structure-based knowledge representations.
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Some active learning algorithms are built upon support-vector machines (SVMs) and exploit the structure of the SVM to determine which data points to label. Such methods usually calculate the margin , W , of each unlabeled datum in T U,i and treat W as an n -dimensional distance from that datum to the separating hyperplane.
Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. This operation is often ...