Abraham Heifets

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This paper describes an experimental system in which customized high performance XML parsers are prepared using parser generation and compilation techniques. Parsing is integrated with Schema-based validation and deserialization, and the resulting validating processors are shown to be as fast as or in many cases significantly faster than traditional(More)
XML, as a data interchange technology, delivers key advantages in interoperability due to its flexibility , expressiveness, and platform-neutrality. The broad range of applications and growing base of users for XML technologies has driven the development of common tooling, providing a consistent, robust infrastructure on which to build applications. These(More)
Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best pre-dictive performance in areas such as speech and image recognition by hierarchically composing simple local features into(More)
With the widespread adoption of SOAP and Web services, XML-based processing, and parsing of XML documents in particular, is becoming a performance-critical aspect of business computing. In such scenarios, XML is often constrained by an XML Schema grammar, which can be used during parsing to improve performance. Although traditional grammar-based parser(More)
XML delivers key advantages in interoperability due to its flexibility, expressiveness, and platform-neutrality. As XML has become a performance-critical aspect of the next generation of business computing infrastructure, however, it has become increasingly clear that XML parsing often carries a heavy performance penalty, and that current, widely-used(More)
The patent literature is a rich catalog of biologically relevant chemicals; many public and commercial molecular databases contain the structures disclosed in patent claims. However, patents are an equally rich source of metadata about bioactive molecules, including mechanism of action, disease class, homologous experimental series, structural alternatives,(More)
Overview We've organized our presentation into three stages: 1. A more detailed coverage of the building blocks of CNNs 2. Attempts to explain how and why Residual Networks work 3. Survey extensions to ResNets and other notable architectures Topics covered: ● Alternative activation functions ● Relationship between fully connected layers and convolutional(More)
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