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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.
The generation effect has been found in studies using free recall, cued recall, and recognition tests. [3] In one study, the subject was provided with a stimulus word, the first letter of the response, and a word relating the two. For example, with the rule of the opposite, the stimulus word "hot", and the letter "c", the word cold would be ...
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 ...
For example, GPT-3, and its precursor GPT-2, [11] are auto-regressive neural language models that contain billions of parameters, BigGAN [12] and VQ-VAE [13] which are used for image generation that can have hundreds of millions of parameters, and Jukebox is a very large generative model for musical audio that contains billions of parameters.
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 ...
The Oscillator Based Associative Recall (OSCAR) Model was proposed by Browne, Preece and Hulme in 2000 [7] The OSCAR Model is another cue driven model of memory. In this model, the cues work as a pointer to a memory’s position in the mind. Memories themselves are stored as context vectors on what Brown calls the oscillator part of the theory.
Generative grammar promotes a modular view of the mind, considering language as an autonomous mind module. Thus, language is separated from mathematical logic to the extent that inference cannot explain language acquisition. [13] The generative conception of human cognition is also influential in cognitive psychology and computer science. [14]