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Deep Learning Stage [clear filter]
Thursday, January 24


Applying ML & NLP in Google Ads
Building and deploying machine-learning (ML) models at Google comes with interesting challenges. For example, some models have to handle massive amounts of training data, while some supervised tasks have insufficient amount of training labels. Or, even when the model quality is good enough for a product requirement, it may not meet other requirements (e.g., serving latency, memory footprint). In this talk we will discuss some of these challenges and share our experiences from deploying ML models for quality improvements in Search Ads products via some case studies. One particular case study I will discuss in detail is a recent paper where we use deep neural networks to understand ad performance and attribute it to particular parts of ad text. This is an interesting research problem in Natural Language Processing (NLP) -- we will outline our key results related to this problem, and discuss interesting areas of future research.

avatar for Sugato Basu, Google

Sugato Basu, Google

Senior Staff Research Scientist/Tech Lead of AdsAI, Google
Dr. Sugato Basu is currently the Tech Lead of the AdsAI team in Google, which applies state-of-the-art machine learning (ML) and natural language processing (NLP) technology to challenging problems in Search Ads at Google. He joined Google in 2007 and has worked for more than a decade... Read More →

Thursday January 24, 2019 1:55pm - 2:20pm
Grand Ballroom Hyatt Regency San Francisco, 5 Embarcadero Center, San Francisco, CA 94111, USA


On-device Neural Networks for Natural Language Processing
Deep neural networks reach state-of-the-art performance for wide range of Natural Language Processing, Computer Vision and Speech applications. Yet, one of the biggest challenges is running these complex networks on devices with tiny memory footprint and low computational capacity such as mobile phones, smart watches and Internet of Things. In this talk, I will introduce novel on-device Self-Governing Neural Networks (SGNNs), which learn compact projection vectors with local sensitive hashing. The key advantage of SGNNs over existing work is that they surmount the need for pre-trained word embeddings and complex networks with huge parameters. I will showcase results from extensive evaluations on wide range of natural language tasks such as dialog act classification and user intent prediction. Our findings show that SGNNs are effective at capturing low-dimensional semantic text representations, while maintaining high accuracy and improving over state-of-the-art results.

avatar for Zornitsa Kozareva, Google

Zornitsa Kozareva, Google

Manager, Google
Dr. Zornitsa Kozareva is a Manager at Google, leading and managing the Natural Language Understanding group and efforts in Google Apps Intelligence. Prior to that, Dr. Kozareva was Manager of Amazon’s AWS Deep Learning group that built and launched the Natural Language Processing... Read More →

Thursday January 24, 2019 2:20pm - 2:40pm
Grand Ballroom Hyatt Regency San Francisco, 5 Embarcadero Center, San Francisco, CA 94111, USA