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Full-Text Articles in Physical Sciences and Mathematics
Neuroscience-Inspired Dynamic Architectures, Catherine Dorothy Schuman
Neuroscience-Inspired Dynamic Architectures, Catherine Dorothy Schuman
Doctoral Dissertations
Biological brains are some of the most powerful computational devices on Earth. Computer scientists have long drawn inspiration from neuroscience to produce computational tools. This work introduces neuroscience-inspired dynamic architectures (NIDA), spiking neural networks embedded in a geometric space that exhibit dynamic behavior. A neuromorphic hardware implementation based on NIDA networks, Dynamic Adaptive Neural Network Array (DANNA), is discussed. Neuromorphic implementations are one alternative/complement to traditional von Neumann computation. A method for designing/training NIDA networks, based on evolutionary optimization, is introduced. We demonstrate the utility of NIDA networks on classification tasks, a control task, and an anomaly detection task. There …
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 …
Learning With Joint Inference And Latent Linguistic Structure In Graphical Models, Jason Narad
Learning With Joint Inference And Latent Linguistic Structure In Graphical Models, Jason Narad
Doctoral Dissertations
Constructing end-to-end NLP systems requires the processing of many types of linguistic information prior to solving the desired end task. A common approach to this problem is to construct a pipeline, one component for each task, with each system's output becoming input for the next. This approach poses two problems. First, errors propagate, and, much like the childhood game of "telephone", combining systems in this manner can lead to unintelligible outcomes. Second, each component task requires annotated training data to act as supervision for training the model. These annotations are often expensive and time-consuming to produce, may differ from each …