Greg Corrado
Mountain View, California, United States
3K followers
500+ connections
About
Greg Corrado is a Distinguished Scientist at Google Research. He joined Google to push…
Activity
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I am excited to announce that my team at Verily recently listed our Verily Retinal Camera with the FDA. This has been a long journey towards the…
I am excited to announce that my team at Verily recently listed our Verily Retinal Camera with the FDA. This has been a long journey towards the…
Liked by Greg Corrado
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We just published the largest synapse-resolution map of brain connectivity that has ever been produced, in any species: https://lnkd.in/gxs6uwb…
We just published the largest synapse-resolution map of brain connectivity that has ever been produced, in any species: https://lnkd.in/gxs6uwb…
Liked by Greg Corrado
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At the Computer History Museum: AI and Social Good. IMHO, AI is fundamental to human survival. There may be problems, such as there are with misuse…
At the Computer History Museum: AI and Social Good. IMHO, AI is fundamental to human survival. There may be problems, such as there are with misuse…
Liked by Greg Corrado
Experience
Education
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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
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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.
Other authorsSee publication
Patents
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Determining a viewing distance for a computing device
US US20140233806
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Integrate and fire electronic neurons
US US8473439
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Region-specific user input
US US20130016129
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Scoring concept terms using a deep network
US US20140279773
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Training a model using parameter server shards
US US8768870
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User input combination of touch and user position
US US8368723
More activity by Greg
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Thanks for having me back for one night Google! It was great to hearing from Urs Hölzle (Google Cloud/Technical Infrastructure) Bradley Horowitz…
Thanks for having me back for one night Google! It was great to hearing from Urs Hölzle (Google Cloud/Technical Infrastructure) Bradley Horowitz…
Liked by Greg Corrado
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