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Latent learning is the subconscious retention of information without reinforcement or motivation. In latent learning, one changes behavior only when there is sufficient motivation later than when they subconsciously retained the information.
Other latent variables correspond to abstract concepts, like categories, behavioral or mental states, or data structures. The terms hypothetical variables or hypothetical constructs may be used in these situations. The use of latent variables can serve to reduce the dimensionality of data. Many observable variables can be aggregated in a model ...
Edward Chace Tolman (April 14, 1886 – November 19, 1959) was an American psychologist and a professor of psychology at the University of California, Berkeley. [1] [2] Through Tolman's theories and works, he founded what is now a branch of psychology known as purposive behaviorism.
A latent variable model is a statistical model that relates a set of observable variables (also called manifest variables or indicators) [1] to a set of latent variables. Latent variable models are applied across a wide range of fields such as biology, computer science, and social science. [ 2 ]
When LDA machine learning is employed, both sets of probabilities are computed during the training phase, using Bayesian methods and an Expectation Maximization algorithm. LDA is a generalization of older approach of probabilistic latent semantic analysis (pLSA), The pLSA model is equivalent to LDA under a uniform Dirichlet prior distribution.
Latent spaces are usually fit via machine learning, and they can then be used as feature spaces in machine learning models, including classifiers and other supervised predictors. The interpretation of the latent spaces of machine learning models is an active field of study, but latent space interpretation is difficult to achieve.
The conditional VAE (CVAE), inserts label information in the latent space to force a deterministic constrained representation of the learned data. [15] Some structures directly deal with the quality of the generated samples [16] [17] or implement more than one latent space to further improve the representation learning.
Machine learning models only have to fit relatively simple, low-dimensional, highly structured subspaces within their potential input space (latent manifolds). Within one of these manifolds, it’s always possible to interpolate between two inputs, that is to say, morph one into another via a continuous path along which all points fall on the ...