Sep 9–12, 2019

Schedule: Models and Methods sessions

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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 E
Lukas Biewald (Weights and Biases)
Introduction to building and deploying LSTMs, GRUs and other text classification techniques using Keras and Scikit Learn. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 C/D
Skyler Thomas (MapR)
The popular open source Kubeflow project is one of the best ways to start doing machine learning and AI on top of Kubernetes. However, Kubeflow is a huge project with dozens of large complex components. In this hands-on session, we will learn about the Kubeflow components and how they interact with Kubernetes. We explore the machine learning lifecycle from model training to model serving. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: LL21 A/B
Neil Conway (Determined AI), Yoav Zimmerman (Determined AI)
Success with deep learning requires understanding more than just TensorFlow or Keras. In this tutorial, we will describe a range of practical problems faced by DL practitioners and the software tools and techniques needed to address them, including data prep/augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile/edge optimization. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: 230 C
Joy Rimchala (Intuit), TJ Torres (Intuit), Xiao Xiao (Intuit), Hui Wang (Intuit)
Document Understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of Data Scientists will describe the design and modeling methodologies used to build this platform-as-a-service. Read more.
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1:45pm2:25pm Wednesday, September 11, 2019
Location: 230 C
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Sequence to Sequence (S2S) modeling using neural networks has been increasingly becoming mainstream in the recent years. In particular, it has been leveraged for applications such as, speech recognition, language translation and question answering. we shall walk through how S2S modeling can be leveraged for the aforementioned use cases, viz., real-time anomaly detection and forecasting. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: 230 B
Wei Cai (Cox Communication )
Real-time traffic volume prediction plays a vital role in proactive network management, and many forecasting models have been proposed to address this issue in the literature. However, most of them suffer from the inability to fully use the rich information in traffic data to generate efficient and accurate traffic predictions for a longer term (i.e., 7 day predictions at a 5-min interval). Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: 230 C
Mark Weber (MIT-IBM Watson AI Lab)
Organized crime inflicts human suffering on a genocidal scale: upwards of 700,000 people per year are "exported" in a $40 billion human trafficking industry enslaving an estimated 40 million people. Such nefarious industries rely on sophisticated money laundering schemes to operate. A new field of AI called graph convolutional networks can help. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 B
Li Erran Li (Pony.ai)
Tremendous progresses have been made in applying machine learning to autonomous driving. I will present recent advances in applying machine learning to solving the perception, prediction, planning and control problems of autonomous driving. I will discuss key research challenges. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 C
Stacy Ashworth (SelectData), Alberto Andreotti (John Snow Labs)
A lot of business data is still scanned or snapped documents. This is a real-world case study on reading, understanding, classifying, and acting on facts extracted from such image files - using state-of-the-art, open source, deep learning based OCR, NLP, and forecasting libraries at scale. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: Expo Hall 3
Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft)
Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is seen as a fundamental shift in which organizations can approach making machine learning. In this talk, you will learn how to use AutoML to automate selection of machine learning models and automate tuning of hyperparameters. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: LL21 C/D
Sijun He (Twitter)
Twitter is what’s happening in the world right now. To connect users with the best content, Twitter needs to build up a deep understanding of its noisy and temporal text content. Sijun He provides an overview of the Named Entity Recognition system at Twitter and discusses the challenges we face to build and scale a large-scale deep learning system to annotate 500 million tweets per day. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: 230 B
Anusua Trivedi (Microsoft)
In this session, we capture a comprehensive study of existing text transfer learning literature in the research community. We explore popular Machine Reading Comprehension (MRC) algorithms. We evaluate and compare the performance of transfer learning approach for creating a QA system for a book corpus using the pretrained MRC models. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: LL21 C/D
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
In this talk we demystify AI explainability through a practical hands-on case study. Our objective will be to automate a loan approval process by building and evaluating a deep learning model. We'll introduce motivations through the practical risks that arise with "undesired bias" & "black box models", and we will show tackle these challenges using tools from latest research and domain knowledge. Read more.
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4:00pm4:40pm Thursday, September 12, 2019
Location: 230 C
Shourabh Rawat (Trulia)
360-degree images have become ubiquitous in industries ranging from real estate to travel. They enable an immersive experience that benefits consumers but creates a challenge for businesses: how do you direct viewers to the most important parts of the scene? In this session, attendees will learn to identify and extract engaging static 2D images using specific algorithms and deep learning methods. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: 230 B
Ramsundar Janakiraman (Aruba Networks, A HPE Company)
While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in embeddings to capture semantics, even that of polysemous words, presents an opportunity for capturing access semantics to model user behavior. With strong embeddings as a foundation, behavioral use-cases could be mapped to NLP models of choice. Read more.

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