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In computer science, a generator is a routine that can be used to control the iteration behaviour of a loop. All generators are also iterators. [1] A generator is very similar to a function that returns an array, in that a generator has parameters, can be called, and generates a sequence of values.
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.
Generative AI features have been integrated into a variety of existing commercially available products such as Microsoft Office (Microsoft Copilot), [85] Google Photos, [86] and the Adobe Suite (Adobe Firefly). [87] Many generative AI models are also available as open-source software, including Stable Diffusion and the LLaMA [88] language model.
A loop invariant is an assertion which must be true before the first loop iteration and remain true after each iteration. This implies that when a loop terminates correctly, both the exit condition and the loop invariant are satisfied. Loop invariants are used to monitor specific properties of a loop during successive iterations.
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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.The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [1]
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