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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.
Generative adversarial network; Flow-based generative model; Energy based model; Diffusion model; If the observed data are truly sampled from the generative model, then fitting the parameters of the generative model to maximize the data likelihood is a common method.
Concentrating on a task, one aspect of flow. Flow in positive psychology, also known colloquially as being in the zone or locked in, is the mental state in which a person performing some activity is fully immersed in a feeling of energized focus, full involvement, and enjoyment in the process of the activity.
Positive psychology is the scientific ... according to Csikszentmihalyi's flow model. ... organizational scholarship focuses attention on the generative dynamics in ...
Related models and techniques are, among others, latent semantic indexing, independent component analysis, probabilistic latent semantic indexing, non-negative matrix factorization, and Gamma-Poisson distribution. The LDA model is highly modular and can therefore be easily extended. The main field of interest is modeling relations between topics.
Generative concern leads to concrete goals and actions such as "providing a narrative schematic of the generative self to the next generation". [7] McAdams and de St. Aubin developed a 20-item scale to assess generativity, and to help discover who it is that is nurturing and leading the next generation. [4]
Generative science is an area of research that explores the natural world and its complex behaviours. It explores ways "to generate apparently unanticipated and infinite behaviour based on deterministic and finite rules and parameters reproducing or resembling the behavior of natural and social phenomena". [ 1 ]
The free energy principle is a theoretical framework suggesting that the brain reduces surprise or uncertainty by making predictions based on internal models and updating them using sensory input. It highlights the brain's objective of aligning its internal model and the external world to enhance prediction accuracy.