Exemplar-Driven Analogy in Optimality Theory

Analogy-driven 3D style transfer

Style transfer aims to apply the style of an exemplar model to a target one, while retaining the target’s structure. The main challenge in this process is to algorithmically distinguish style from structure, a high-level, potentially ill-posed cognitive task. Inspired by cognitive science research we recast style transfer in terms of shape analogies. In IQ testing, shape analogy queries present...

From Exemplar Theory to Population Coding and Back. An Ideal Observer Approach

Exploiting the analogy between exemplar models and population coding schemes, we characterize, by means of information theoretic tools, the efficiency of a large but finite population of cells coding for a discrete set of categories (e.g. vowels). The optimal code is shown to typically allocate more cells to class boundaries than to regions further apart. We then discuss the predicted perceptua...

Learning Abstract Relations Through Analogy to Concrete Exemplars

We present a model that bridges work in analogy and category learning. The model, Building Relations through Instance Driven Gradient Error Shifting (BRIDGES), extends ALCOVE, an exemplar-based connectionist model of category learning (Kruschke, 1992). Unlike ALCOVE which is limited to featural or spatial representations, BRIDGES can appreciate analogical relationships between stimuli and store...

Optimality Of Monetary And Fiscal Policies In Iran: An Application Of The Stochastic Optimal Control Theory

Nonlinear Planning Model With a Gaussian Criterion of Optimization (Gaussian Programming Model)

We propose an Economic Probabilistic analogy: the category of cost is analogous to the category of Probability. The proposed analogy permits construction of an informal theory of nonlinear non-convex Gaussian Utility and Cost, which describes the real economic processes more adequately than a theory based on a linear and convex models. Based on the proposed analogy, we build a nonlinear non-con...