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Artificial Intelligence and Robotics Commons™
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- Artificial Intelligence (1)
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- Causal Inference (1)
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- Efficient inference (1)
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Articles 1 - 3 of 3
Full-Text Articles in Artificial Intelligence and Robotics
Bayesian Structural Causal Inference With Probabilistic Programming, Sam A. Witty
Bayesian Structural Causal Inference With Probabilistic Programming, Sam A. Witty
Doctoral Dissertations
Reasoning about causal relationships is central to the human experience. This evokes a natural question in our pursuit of human-like artificial intelligence: how might we imbue intelligent systems with similar causal reasoning capabilities? Better yet, how might we imbue intelligent systems with the ability to learn cause and effect relationships from observation and experimentation? Unfortunately, reasoning about cause and effect requires more than just data: it also requires partial knowledge about data generating mechanisms. Given this need, our task then as computational scientists is to design data structures for representing partial causal knowledge, and algorithms for updating that knowledge in …
Efficient Inference, Search And Evaluation For Latent Variable Models Of Text With Applications To Information Retrieval And Machine Translation, Kriste Krstovski
Efficient Inference, Search And Evaluation For Latent Variable Models Of Text With Applications To Information Retrieval And Machine Translation, Kriste Krstovski
Doctoral Dissertations
Latent variable models of text, such as topic models, have been explored in many areas of natural language processing, information retrieval and machine translation to aid tasks such as exploratory data analysis, automated topic clustering and finding similar documents in mono- and multilingual collections. Many additional applications of these models, however, could be enabled by more efficient techniques for processing large datasets. In this thesis, we introduce novel methods that offer efficient inference, search and evaluation for latent variable models of text. We present efficient, online inference for representing documents in several languages in a common topic space and fast …
Epistemological Databases For Probabilistic Knowledge Base Construction, Michael Louis Wick
Epistemological Databases For Probabilistic Knowledge Base Construction, Michael Louis Wick
Doctoral Dissertations
Knowledge bases (KB) facilitate real world decision making by providing access to structured relational information that enables pattern discovery and semantic queries. Although there is a large amount of data available for populating a KB; the data must first be gathered and assembled. Traditionally, this integration is performed automatically by storing the output of an information extraction pipeline directly into a database as if this prediction were the ``truth.'' However, the resulting KB is often not reliable because (a) errors accumulate in the integration pipeline, and (b) they persist in the KB even after new information arrives that could rectify …