一家美国的跨国科技企业,致力于互联网搜索、云计算、广告技术等领域,由当时在斯坦福大学攻读理学博士的拉里·佩奇和谢尔盖·布林共同创建。创始之初,Google 官方的公司使命为「整合全球范围的信息,使人人皆可访问并从中受益」。 Google 开发并提供了大量基于互联网的产品与服务,其主要利润来自于 AdWords 等广告服务。

2004 年 8 月 19 日, 公司以「GOOG」为代码正式登陆纳斯达克交易所。

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https://gm-neurips-2020.github.io/

在这次演讲中,Graph Mining team的创始人Vahab对本图挖掘和学习进行了高层次的介绍。这个演讲涉及到什么是图,为什么它们是重要的,以及它们在大数据世界中的位置。然后讨论了组成图挖掘和学习工具箱的核心工具,并列出了几个规范的用例。它还讨论了如何结合算法、系统和机器学习来在不同的分布式环境中构建一个可扩展的图学习系统。最后,它提供了关于Google一个简短的历史图挖掘和学习项目。本次演讲将介绍接下来的演讲中常见的术语和主题。

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We introduce an unsupervised approach for correcting highly imperfect speech transcriptions based on a decision-level fusion of stemming and two-way phoneme pruning. Transcripts are acquired from videos by extracting audio using Ffmpeg framework and further converting audio to text transcript using Google API. In the benchmark LRW dataset, there are 500 word categories, and 50 videos per class in mp4 format. All videos consist of 29 frames (each 1.16 s long) and the word appears in the middle of the video. In our approach we tried to improve the baseline accuracy from 9.34% by using stemming, phoneme extraction, filtering and pruning. After applying the stemming algorithm to the text transcript and evaluating the results, we achieved 23.34% accuracy in word recognition. To convert words to phonemes we used the Carnegie Mellon University (CMU) pronouncing dictionary that provides a phonetic mapping of English words to their pronunciations. A two-way phoneme pruning is proposed that comprises of the two non-sequential steps: 1) filtering and pruning the phonemes containing vowels and plosives 2) filtering and pruning the phonemes containing vowels and fricatives. After obtaining results of stemming and two-way phoneme pruning, we applied decision-level fusion and that led to an improvement of word recognition rate upto 32.96%.

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