Jeffrey Lund

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Topic models provide insights into document collections, and their supervised extensions also capture associated document-level metadata such as sentiment. However, inferring such models from data is often slow and cannot scale to big data. We build upon the “anchor” method for learning topic models to capture the relationship between metadata and latent(More)
The paper describes an experimental software bridge that exposes LonWorks/sup /spl reg// devices as UPnP/spl trade/ devices to UPnP control points. By UPnP enabling common LonWorks devices, the bridge expands the reach of UPnP technology into the pervasive world of non-IP-based everyday devices. Mapping rules and design decisions implemented in the bridge(More)
A design and verification methodology of advanced SRAM bitcell design is described. Dense bitcells, drawn for embedded SRAM memory applications, are drawn and simulated for cell functionality and stability. After first-pass design, lithographic correction is determined using analytical and iterative simulation routines. Analytical corrections are tailored(More)
The MapReduce parallel programming model is designed for large-scale data processing, but its benefits, such as fault tolerance and automatic message routing, are also helpful for computationally-intensive algorithms. However, popular MapReduce frameworks such as Hadoop are slow for many scientific applications and are inconvenient on supercomputers and(More)
Interactive topic models are powerful tools for understanding large collections of text. However, existing sampling-based interactive topic modeling approaches scale poorly to large data sets. Anchor methods, which use a single word to uniquely identify a topic, offer the speed needed for interactive work but lack both a mechanism to inject prior knowledge(More)
Mrs [1] is a lightweight Python-based MapReduce implementation designed to make MapReduce programs easy to write and quick to run, particularly useful for research and academia. A common set of algorithms that would benefit from Mrs are iterative algorithms, like those frequently found in machine learning; however, iterative algorithms typically perform(More)
Probabilistic models are a useful means for analyzing large text corpora. Integrating such models with human interaction enables many new use cases. However, adding human interaction to probabilistic models requires inference algorithms which are both fast and accurate. We explore the use of Iterated Conditional Modes as a fast alternative to Gibbs sampling(More)
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