Greg Corrado

Greg Corrado

Mountain View, California, United States
3K followers 500+ connections

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About

Greg Corrado is a Distinguished Scientist at Google Research. He joined Google to push…

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Experience

Education

  • Stanford University Graphic

    Stanford University

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    Systems and Computational Neuroscience, specializing in mathematical modeling of value-based decision making.

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    Artificial Intelligence Concentration

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    Activities and Societies: Terrace Club

Publications

  • BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

    NIPS Deep Learning Workshop

    We introduce BilBOWA ("Bilingual Bag-of-Words without Alignments"), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large datasets and does not require word-aligned training data. Instead it trains directly on monolingual data and extracts a bilingual signal from a smaller set of raw text sentence-aligned data. This is achieved using a novel sampled bag-of-words cross-lingual objective, which is used to regularize two…

    We introduce BilBOWA ("Bilingual Bag-of-Words without Alignments"), a simple and computationally-efficient model for learning bilingual distributed representations of words which can scale to large datasets and does not require word-aligned training data. Instead it trains directly on monolingual data and extracts a bilingual signal from a smaller set of raw text sentence-aligned data. This is achieved using a novel sampled bag-of-words cross-lingual objective, which is used to regularize two noise-contrastive language models for efficient cross-lingual feature learning. We show that bilingual embeddings learned using the proposed model outperforms state-of-the-art methods on a cross-lingual document classification task as well as a lexical translation task on the WMT11 data. Our code will be made available as part of the open-source word2vec toolkit.

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Patents

  • Determining a viewing distance for a computing device

    US US20140233806

  • Integrate and fire electronic neurons

    US US8473439

  • Region-specific user input

    US US20130016129

  • Scoring concept terms using a deep network

    US US20140279773

  • Training a model using parameter server shards

    US US8768870

  • User input combination of touch and user position

    US US8368723

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