Put AI to Work
April 15-18, 2019
New York, NY

Schedule: Temporal data and time-series sessions

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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft), Krishna Anumalasetty (Microsoft)
Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources. Read more.
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1:45pm5:15pm Tuesday, April 16, 2019
Implementing AI
Location: Trianon Ballroom
Bruno Goncalves (JPMorgan Chase)
Average rating: ****.
(4.00, 1 rating)
Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonçalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches. Read more.
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1:00pm1:40pm Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
JIAN CHANG (Alibaba Group), Sanjian Chen (Alibaba Group)
Average rating: *****
(5.00, 1 rating)
Jian Chang and Sanjian Chen outline the design of the AI engine built on Alibaba’s TSDB service, which enables fast and complex analytics of large-scale time series data in many business domains. Join in to see how TSDB empowers companies across various industries to better understand data trends, discover anomalies, manage risks, and boost efficiency. Read more.
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1:50pm2:30pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Arun Kejariwal and Ira Cohen share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges you may encounter based on production data. Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Implementing AI
Location: Regent Parlor
Aric Whitewood (WilmotML)
Aric Whitewood details WilmotML's research on the application of AI to investment management and offers an overview of the company's prediction engine, GAIA (the Global AI Allocator), which has been running in production since January 2018. Read more.
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4:55pm5:35pm Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South
Mohammad Mavadati (Affectiva)
According to the CDC, drowsy drivers cause up to 6,000 fatal crashes annually. Driver alertness monitoring systems will enable more reliable vehicles and safer roads. Mohammad Mavadati offers an overview of state-of-the-art vision-based DNN techniques for drowsiness (intensity) annotations and modeling and reveals some of the AI solutions for in-car drowsiness predictions. Read more.