Sep 23–26, 2019

Schedule: Deep Learning sessions

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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1E 07
Dylan Bargteil (The Data Incubator)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil offers an overview of TensorFlow's capabilities in Python, demonstrating how to build machine learning algorithms piece by piece and how to use TensorFlow's Keras API with several hands-on applications. Read more.
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9:00am - 5:00pm Monday, September 23 & Tuesday, September 24
Location: 1A 03
Bargava Subramanian (Binaize Labs), Amit Kapoor (narrativeVIZ)
In this two-days workshop, you will learn the different paradigms of recommendation systems and get introduced to the usage of deep-learning based approaches . By the end of the workshop, you will have enough practical hands-on knowledge to build, select, deploy and maintain a recommendation system for your problem. Read more.
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9:00am12:30pm Tuesday, September 24, 2019
Location: 1E 08
Bruno Goncalves (Data For Science, Inc)
Students will learn, in a hands-on way, the theoretical foundations and principal ideas underlying Deep Learning and Neural Networks. The code structure of the implementations provided is meant to closely resemble he way Keras is structured so that by the end of the course, students will be prepared to dive deeper into the deep learning applications of their choice. Read more.
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1:30pm5:00pm Tuesday, September 24, 2019
Location: 1A 12/14
Garrett Hoffman (StockTwits)
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks. Read more.
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11:20am12:00pm Wednesday, September 25, 2019
Location: 1A 06/07
Ying Yau (AllianceBernstein)
Time series forecasting techniques can be applied in a wide range of scientific disciplines, business scenarios, and policy settings. This session discusses the application of deep learning techniques to time series forecasting and compares them to time series statistical models when forecasting time series with trends, multiple seasonality, regime switch, and exogenous series. Read more.
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1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 06/07
Every NLP based document processing solution depends on converting scanned documents/ images to machine readable text using an OCR solution. However, accuracy of OCR solutions is limited by quality of scanned images. We show that generative adversarial networks can be used to bring significant efficiencies in any document processing solution by enhancing resolution and de-noising scanned images. Read more.
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1:15pm1:55pm Wednesday, September 25, 2019
Location: 1A 12/14
Shioulin Sam (Cloudera Fast Forward Labs)
Supervised machine learning requires large labeled datasets - a prohibitive limitation in many real world applications. What if machines could learn with few labeled examples? This talk explores and demonstrates an algorithmic solution that relies on collaboration between human and machines to label smartly, and discuss product possibilities. Read more.
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2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 06/07
Keshav Peswani (Expedia Group), Ashish Aggarwal (Expedia Group)
Observability is the key in modern architecture to quickly detect and repair problems in microservices. Modern observability platforms have evolved beyond simple application logs and now include distributed tracing systems like Zipkin, Haystack. Combining them with real time intelligent alerting mechanisms with accurate alerts helps in automated detection of these problems. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 21/22
Kai Liu (BING) (Microsoft (BING))
Facilitating large scale of deep learning projects in parallel requires some effort and innovation. Bing is now running a deployment of thousands of servers to address this challenge. We provides training services, offline data processing, vector hosting, and inferencing service at offline fashion to help data scientists through all steps in the project life cycle. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 06/07
Tony Xing (Microsoft), Bixiong Xu (Microsoft), Congrui Huang (Microsoft), Qun Ying (Microsoft)
Anomaly Detection may sound old fashioned yet super important in many industry applications. How about doing this in a computer vision way? Come to our talk to learn a novel Anomaly Detection algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN), and how this novel method was applied in the monitoring system supporting Microsoft AIOps and business incident prevention. Read more.
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4:35pm5:15pm Wednesday, September 25, 2019
Location: 1A 06/07
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Optimizing deep neural nets to run efficiently on mobile devices. Read more.
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5:25pm6:05pm Wednesday, September 25, 2019
Location: 1A 06/07
The common perception of deep learning is that it results in a fully self-contained model. However, in most cases these models have similar requirements for data pre-processing as more "traditional" machine learning. Despite this, there are few standard solutions for deploying end-to-end deep learning. In this talk, I show how the ONNX format and ecosystem is addressing this challenge. Read more.
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1:15pm1:55pm Thursday, September 26, 2019
Location: 3B - Expo Hall
Victor Dibia (Cloudera Fast Forward Labs)
Recent advances in Machine Learning frameworks for the browser such as Tensorflow.js provides opportunity to craft truly novel experiences within front-end applications. This talk explores the state of the art for Machine Learning in the browser using Tensorflow.js and covers its use in the design of Handtrack.js - a library for prototyping real time hand detection in the browser. Read more.
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2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 06/07
Ryan Foltz (Exabeam)
Unmanaged & foreign devices in the corporate networks pose a security risk. The 1st step toward reducing risk from these devices is the ability to identify them. To have a comprehensive device management program, we proposed a machine learning model based on Deep Learning to perform anomaly detection based on only device names to flag devices that do not follow device naming structures. Read more.
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3:45pm4:25pm Thursday, September 26, 2019
Location: 1A 06/07
Sajan Govindan (Intel), Luca Canali (CERN)
We will show CERN’s research on applying Deep Learning in High Energy Physics experiments as an alternative to customized rule based methods with an example of topology classification to improve real-time event selection at the Large Hadron Collider experiments. CERN implemented deep learning pipelines on Apache Spark using BigDL and Analytics Zoo open source software on Intel Xeon-based clusters Read more.
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4:35pm5:15pm Thursday, September 26, 2019
Location: 1A 06/07
Naoto Umemori (NTT DATA Corporation), Masaru Dobashi (NTT Data Corp.)
Giant Hogweed is a highly toxic plant. Our project aims to automate the process of detecting the Giant Hogweed by exploiting technologies like drones and image recognition/detection using Machine Learning. We show you how we designed the architecture, how we took advantage of both of Big Data and Machine / Deep Learning technologies (e.g. Hadoop, Spark and TensorFlow) and lessons learned. Read more.

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