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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.
Regardless of precise definition, the terminology is constitutional because a generative model can be used to "generate" random instances , either of an observation and target (,), or of an observation x given a target value y, [2] while a discriminative model or discriminative classifier (without a model) can be used to "discriminate" the ...
Mannheim defined a generation (note that some have suggested that the term cohort is more correct) to distinguish social generations from the kinship (family, blood-related generations) [2] as a group of individuals of similar ages whose members have experienced a noteworthy historical event within a set period of time.
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 ]
Collective memory has been conceptualized in several ways and proposed to have certain attributes. For instance, collective memory can refer to a shared body of knowledge (e.g., memory of a nation's past leaders or presidents); [6] [7] [8] the image, narrative, values and ideas of a social group; or the continuous process by which collective memories of events change.
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]
The model takes into account the transmission of prediction errors to the same level or a level above, in order to minimise the energy function that indicates the difference between the data and its cause, or, in other words, between the generative model and the posterior.
The Cambridge Dictionary of Sociology is a dictionary of sociological terms published by Cambridge University Press and edited by Bryan S. Turner. There has only been one edition so far. The Board of Editorial Advisors is made up of: Bryan S. Turner, Ira Cohen, Jeff Manza, Gianfranco Poggi, Beth Schneider, Susan Silbey, and Carol Smart. In ...