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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.
Analogously, a classifier based on a generative model is a generative classifier, while a classifier based on a discriminative model is a discriminative classifier, though this term also refers to classifiers that are not based on a model. Standard examples of each, all of which are linear classifiers, are: generative classifiers:
The generative model is the specification of the following density functions: A sensory model, p S : S × Ψ × A → R {\displaystyle p_{S}:S\times \Psi \times A\to \mathbb {R} } , often written as p S ( s ∣ ψ , a ) {\displaystyle p_{S}(s\mid \psi ,a)} , characterizing the likelihood of sensory data given external states and actions;
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]
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 ...
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.
Stock and flow diagrams - a way to quantify the structure of a dynamic system; These methods allow showing a mental model of a dynamic system, as an explicit, written model about a certain system based on internal beliefs. Analyzing these graphical representations has been an increasing area of research across many social science fields. [9]
Memristive networks are a particular type of physical neural network that have very similar properties to (Little-)Hopfield networks, as they have continuous dynamics, a limited memory capacity and natural relaxation via the minimization of a function which is asymptotic to the Ising model. In this sense, the dynamics of a memristive circuit ...