Sep 9–12, 2019

Schedule: Models and Methods sessions

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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 E/F
Secondary topics:  Deep Learning, Deep Learning tools, Machine Learning, Text, Language, and Speech
Lukas Biewald (Weights & Biases)
Join Lukas Biewald to build and deploy long short-term memories (LSTMs), grated recurrent units (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
Secondary topics:  Deep Learning tools, Machine Learning
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. Skyler Thomas dives into the Kubeflow components and how they interact with Kubernetes. He explores 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
Secondary topics:  Deep Learning, Deep Learning tools, Hardware, Machine Learning
Neil Conway (Determined AI), Yoav Zimmerman (Determined AI)
Success with DL requires more than just TensorFlow or Keras. Neil Conway and Yoav Zimmerman detail a range of practical problems faced by DL practitioners and the software tools and techniques you'll need to address the problems, including data prep and augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile and edge optimization. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: 230 A
Secondary topics:  Computer Vision, Data, Data Networks, Data Quality, Machine Learning, Text, Language, and Speech
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, Joy Rimchala, TJ Torres, Xiao Xiao, and Hui Wang, detail 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 A
Secondary topics:  Deep Learning, Machine Learning, Temporal data and time-series
Arun Kejariwal (Facebook), Ira Cohen (Anodot)
Sequence to sequence (S2S) modeling using neural networks is increasingly becoming mainstream. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for the aforementioned use cases, visualization, real-time anomaly detection, and forecasting. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: 230 C
Secondary topics:  Deep Learning, Machine Learning, Temporal data and time-series
Wei Cai (Cox Communications)
Real-time traffic volume prediction is vital in proactive network management, and many forecasting models have been proposed to address this. However, most are unable to fully use the information in traffic data to generate efficient and accurate traffic predictions for a longer term. Wei Cai explores predicting multistep, real-time traffic volume using many-to-one LSTM and many-to-many LSTM. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: 230 A
Secondary topics:  Machine Learning
Mark Weber (MIT-IBM Watson AI Lab)
Organized crime inflicts human suffering on a massive scale: upward 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. Mark Weber explores how 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 C
Secondary topics:  Deep Learning, Machine Learning, Mobile Computing, IoT, Edge, Reinforcement Learning
Li Erran Li (Pony.ai | Columbia University)
Tremendous progress has been made in applying machine learning to autonomous driving. Li Erran Li explores recent advances in applying machine learning to solving the perception, prediction, planning, and control problems of autonomous driving as well as some key research challenges. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 A
Secondary topics:  Computer Vision, Deep Learning, Machine Learning, Mobile Computing, IoT, Edge, Text, Language, and Speech
Stacy Ashworth (SelectData), Alberto Andreotti (John Snow Labs)
Much business data is still scanned or snapped documents, which is challenging. Stacy Ashworth and Alberto Andreotti explore a real-world case on reading, understanding, classifying, and acting on facts extracted from such image files using state-of-the-art, open source, deep learning-based optical character recognition (OCR), natural language processing (NLP), and forecasting libraries at scale. Read more.
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4:50pm5:30pm Wednesday, September 11, 2019
Location: 230 C
Secondary topics:  Data, Data Networks, Data Quality, Deep Learning, Machine Learning, Text, Language, and Speech
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 (NER) system at Twitter and explores the challenges Twitter faces to build and scale a large-scale deep learning system to annotate 500 million tweets per day. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: Expo Hall 3
Secondary topics:  Machine Learning, Temporal data and time-series
Francesca Lazzeri (Microsoft)
Automated machine learning (AutoML) enables data scientists and domain experts to be productive and efficient. AutoML is seen as a fundamental shift in the way in which organizations can approach machine learning. Francesca Lazzeri outlines how to use AutoML to automate machine learning model selection and automate hyperparameter tuning. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: 230 C
Secondary topics:  Design, Interfaces, and UX, Machine Learning, Text, Language, and Speech
Anusua Trivedi (Microsoft)
Modern machine learning models often significantly benefit from transfer learning. Anusua Trivedi details a study of existing text transfer learning literature. She explores popular machine reading comprehension (MRC) algorithms and evaluates and compares the performance of the transfer learning approach for creating a question answering (QA) system for a book corpus using pretrained MRC models. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: LL21 C/D
Secondary topics:  Ethics, Security, and Privacy, Machine Learning
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge. Read more.
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4:00pm4:40pm Thursday, September 12, 2019
Location: 230 A
Secondary topics:  Computer Vision, Deep Learning, Machine Learning
Shourabh Rawat (Trulia)
Lately, 360-degree images have become ubiquitous in industries from real estate to travel. They enable an immersive experience that benefits consumers but creates a challenge for businesses to direct viewers to the most important parts of the scene. Shourabh Rawat walks you through how to identify and extract engaging static 2-D images using specific algorithms and deep learning methods. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: 230 C
Secondary topics:  Ethics, Security, and Privacy, Machine Learning, Text, Language, and Speech
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 capturing semantics present an opportunity for capturing access semantics to model user behavior. Ram Janakiraman explains how, with strong embeddings as a foundation, behavioral use cases can be mapped to NLP models of choice. Read more.

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