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Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve stateof-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performancedestroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and(More)
Over the past few years, Markov Logic Networks (MLNs) have emerged as a powerful AI framework that combines statistical and logical reasoning. It has been applied to a wide range of data management problems, such as information extraction, ontology matching, and text mining, and has become a core technology underlying several major AI projects. Because of(More)
The Knowledge Base Acceleration track in TREC 2012 focused on a single task: filter a time-ordered corpus for documents that are highly relevant to a predefined list of entities. KBA differs from previous filtering evaluations in two primary ways: the stream corpus is >100x larger than previous filtering collections, and the use of entities as topics(More)
We present an end-to-end (live) demonstration system called DeepDive that performs knowledge-base construction (KBC) from hundreds of millions of web pages. DeepDive employs statistical learning and inference to combine diverse data resources and best-of-breed algorithms. A key challenge of this approach is scalability, i.e., how to deal with terabytes of(More)
Recognizing human activities from image sequences is an active area of research in computer vision. Most of the previous work on activity recognition focuses on recognition from video clips that show only single activities. There are few published algorithms for segmenting and recognizing complex activities that are composed of more than one single(More)
Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. We present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, we have implemented a solution to(More)
Aerosols alter cloud density and the radiative balance of the atmosphere. This leads to changes in cloud microphysics and atmospheric stability, which can either suppress or foster the development of clouds and precipitation. The net effect is largely unknown, but depends on meteorological conditions and aerosol properties. Here, we examine the long-term(More)
Classically, training relation extractors relies on high-quality, manually annotated training data, which can be expensive to obtain. To mitigate this cost, NLU researchers have considered two newly available sources of less expensive (but potentially lower quality) labeled data from distant supervision and crowd sourcing. There is, however, no study(More)
A new generation of data processing systems, including web search, Google’s Knowledge Graph, IBM’s Watson, and several different recommendation systems, combine rich databases with software driven by machine learning. The spectacular successes of these trained systems have been among the most notable in all of computing and have generated excitement in(More)