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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.
By equipping the generative model with hidden states that model control, policies (control sequences) that minimise variational free energy lead to high utility states. [ 51 ] Neurobiologically, neuromodulators such as dopamine are considered to report the precision of prediction errors by modulating the gain of principal cells encoding ...
In 2004, [4] Rick Grush proposed a model of neural perceptual processing according to which the brain constantly generates predictions based on a generative model (what Grush called an ‘emulator’), and compares that prediction to the actual sensory input. The difference, or ‘sensory residual’ would then be used to update the model so as ...
Generativity in technology is defined as “the ability of a technology platform or technology ecosystem to create, generate or produce new output, structure or behavior without input from the originator of the system.” [2] An example of this could be any computing platform, such as the iOS and Android mobile operating systems, for which ...
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 ...
The generation effect is typically achieved in cognitive psychology experiments by asking participants to generate words from word fragments. [2] This effect has also been demonstrated using a variety of other materials, such as when generating a word after being presented with its antonym, [3] synonym, [1] picture, [4] arithmetic problems, [2] [5] or keyword in a paragraph. [6]
Divergent thinking is a thought process used to generate creative ideas by exploring many possible solutions. It typically occurs in a spontaneous, free-flowing, "non-linear" manner, such that many ideas are generated in an emergent cognitive fashion.
According to the Atkinson-Shiffrin memory model or multi-store model, for information to be firmly implanted in memory it must pass through three stages of mental processing: sensory memory, short-term memory, and long-term memory. [7] An example of this is the working memory model. This includes the central executive, phonologic loop, episodic ...