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An entity–relationship model (or ER model) describes interrelated things of interest in a specific domain of knowledge. A basic ER model is composed of entity types (which classify the things of interest) and specifies relationships that can exist between entities (instances of those entity types).
Generative AI can also be trained extensively on audio clips to produce natural-sounding speech synthesis and text-to-speech capabilities, exemplified by ElevenLabs' context-aware synthesis tools or Meta Platform's Voicebox. [55] AI-generated music from the Riffusion Inference Server, prompted with bossa nova with electric guitar
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 ...
Fully Communication Oriented Information Modeling (FCO-IM) is a method for building conceptual information models.Such models can then be automatically transformed into entity-relationship models (ERM), Unified Modeling Language (UML), relational or dimensional models with the FCO-IM Bridge toolset, and it is possible to generate complete end-user applications from them with the IMAGine toolset.
The enhanced entity–relationship (EER) model (or extended entity–relationship model) in computer science is a high-level or conceptual data model incorporating extensions to the original entity–relationship (ER) model, used in the design of databases.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
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A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. [1] [2] The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [3]