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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 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]
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 ...
Situativity theorists suggest a model of knowledge and learning that requires thinking on the fly rather than the storage and retrieval of conceptual knowledge. In essence, cognition cannot be separated from the context. Instead, knowing exists in situ, inseparable from context, activity, people, culture, and language. Therefore, learning is ...
Model A reduces to the models studied in [3] [4] depending on the choice of the activation function, model B reduces to the model studied in, [1] model C reduces to the model of. [ 5 ] General systems of non-linear differential equations can have many complicated behaviors that can depend on the choice of the non-linearities and the initial ...
The Deese–Roediger–McDermott (DRM) paradigm is a procedure in cognitive psychology used to study false memory in humans. The procedure was pioneered by James Deese in 1959, but it was not until Henry L. Roediger III and Kathleen McDermott extended the line of research in 1995 that the paradigm became popular.