We study the problem of compressed sensing with asymmetric prior information, as motivated by networking applications. We focus on the scenario in which a resource-limited encoder needs to report a small subset S from a universe of N objects to a more powerful decoder. The distinguishing feature of our model is asymmetry: the subset S is an i.i.d. sample from a prior distribution μ, and μ is on...

Classical approaches to shape correspondence base their computation purely on the properties, in particular geometric similarity, of the shapes in question. Their performance still falls far short of that of humans in challenging cases where corresponding shape parts may differ significantly in geometry or even topology. We stipulate that in these cases, shape correspondence by humans involves ...

The problem of community detection is a challenging one because of the presence of hubs and noisy links, which tend to create highly imbalanced graph clusters. Often, these resulting clusters are not very intuitive and difficult to interpret. With the growing availability of network information, there is a significant amount of prior knowledge available about the communities in social, communic...

Intelligence test scores can account for achievement differences in many content areas to a considerable extent. An individual’s IQ results from complex interactions between genes and environmental stimulation, foremost schooling. The amount of variance in intelligence to be explained by genes is the higher the more successful a society is in providing cognitively stimulating environments for e...

Fast interceptive actions, such as catching a ball, rely upon accurate and precise information from vision. Recent models rely on flexible combinations of visual angle and its rate of expansion of which the tau parameter is a specific case. When an object approaches an observer, however, its trajectory may introduce bias into tau-like parameters that render these computations unacceptable as th...

The standard so-called experts algorithms are methods for utilizing a given set of “experts” to make good choices in a sequential decision-making problem. In the standard setting of experts algorithms, the decision maker chooses repeatedly in the same “state” based on information about how the different experts would have performed if chosen to be followed. In this paper we seek to extend this ...

Incorporation of prior knowledge into the learning process can significantly improve low-sample classification accuracy. We show how to introduce prior knowledge into linear support vector machines in form of constraints on the rotation of the normal to the separating hyperplane. Such knowledge frequently arises naturally, e.g., as inhibitory and excitatory influences of input variables. We dem...

This paper discusses a general framework called FLARE, that integrates inductive learning using prior knowledge together with reasoning in a non-recursive, propositional setting. FLARE learns incrementally by continually revising its knowledge base in the light of new evidence. Prior knowledge is generally given by a teacher and takes the form of pre-encoded rules. Simple defaults, combined wit...

End users can find topic model results difficult to interpret and evaluate. To address user needs, we present a semi-supervised hierarchical Dirichlet process for topic modeling that incorporates user-defined prior knowledge. Applied to a large electronic dataset, the generated topics are more fine-grained, more distinct, and align better with users’ assignments of topics to documents.

New concepts can be learned by statistical associations, as well as by relevant existing knowledge. We examined the interaction of these two processes by manipulating exemplar frequency and thematic knowledge and considering their interaction through computational modeling. Exemplar frequency affects category learning, with high-frequency items learned more quickly than low-frequency items, and...