October 28–31, 2019

Schedule: Applications sessions

Focusing on real-world TensorFlow implementation, bringing experience and insight from enterprise and research.

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9:00am12:30pm Tuesday, October 29, 2019
Location: Grand Ballroom A/B
Sandeep Gupta (Google), Brijesh Krishnaswami (Google)
Join Sandeep Gupta and Brijesh Krishnaswami to learn how to build and deploy machine learning models using JavaScript, with official documentation, examples, and code labs from the TensorFlow team. Read more.
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1:30pm5:00pm Tuesday, October 29, 2019
Location: Grand Ballroom E
Maggie Zhang (NVIDIA), Nathan Luehr (NVIDIA), Josh Romero (NVIDIA), Pooya Davoodi (NVIDIA), Davide Onofrio (NVIDIA)
Average rating: ****.
(4.00, 1 rating)
Maggie Zhang, Nathan Luehr, Josh Romero, Pooya Davoodi, and Davide Onofrio give you a sneak peek at software components from NVIDIA’s software stack so you can get the best out of your end-to-end AI applications on modern NVIDIA GPUs. They also examine features and tips and tricks to optimize your workloads right from data loading, processing, training, inference, and deployment. Read more.
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11:00am11:40am Wednesday, October 30, 2019
Location: Grand Ballroom A/B
Hamel Husain (GitHub), Omoju Miller (GitHub), Michal Jastrzebski (GitHub), Jeremy Lewi (Google)
Average rating: ****.
(4.50, 2 ratings)
Software development is central to machine learning, regardless of if you're prototyping in a Jupyter notebook or building a service for millions of users. Hamel Husain, Omoju Miller, Michał Jastrzębski, and Jeremy Lewi show you how to use a freely available, natural language dataset to build practical applications for anyone who writes software using TensorFlow. Read more.
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11:00am11:40am Wednesday, October 30, 2019
Location: Grand Ballroom E
Nicolas kowalski (Criteo), Axel Antoniotti (Criteo)
Average rating: *****
(5.00, 2 ratings)
Criteo's real-time bidding of ad spaces requires its TensorFlow (TF) models to make online predictions in less than 5 ms. Nicolas Kowalski and Axel Antoniotti explain why Criteo moved away from high-level APIs and rewrote its models from scratch, reimplementing cross-features and hashing functions using low-level TF operations in order to factorize as much as possible all TF nodes in its model. Read more.
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11:50am12:30pm Wednesday, October 30, 2019
Location: Grand Ballroom E
Moderated by:
Deepak Bhadauria (Google)
Panelists:
Saurabh Mishra (Quantiphi), Upendra Sahu (Quantiphi), Bhushan Jagyasi (Accenture), David Beck (Cognizant), Rahul Sarda (Wipro Limited)
Deepak Bhadauria, Saurabh Mishra, Upendra Sahu, Bhushan Jagyasi, David Beck, and Rahul Sarda share four real-world TensorFlow success stories from the banking, insurance, med tech, and nonprofit industries. Read more.
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1:40pm2:20pm Wednesday, October 30, 2019
Location: Grand Ballroom A/B
Josh Baer (Spotify), Keshi Dai (Spotify)
Average rating: ****.
(4.00, 1 rating)
Josh Baer and Keshi Dai discuss how Spotify has historically used ML and explore how the introduction of TensorFlow and TFX in particular has standardized its ML workflows and improved its ability to bring ML-powered products to its users. Read more.
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2:30pm3:10pm Wednesday, October 30, 2019
Location: Grand Ballroom A/B
Pengfei Fan (Alibaba), Lingling Jin (Alibaba)
Pengfei Fan and Lingling Jin offer an overview of an efficient and elastic GPU-sharing system for users who do research and development with TensorFlow. Read more.
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4:10pm4:50pm Wednesday, October 30, 2019
Location: Grand Ballroom A/B
Wisdom d'Almeida walks you through how to design an encoder-decoder model that takes a chest X-ray image as input and generates a radiology report with visual and textual explanations for interpretability. The model was designed with TensorFlow, trained on cloud TPUs, and deployed in the browser with TensorFlow.js. Wisdom provides a live demo of the model in action. Read more.
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5:00pm5:40pm Wednesday, October 30, 2019
Location: Grand Ballroom A/B
Sean Park (Trend Micro)
Average rating: *****
(5.00, 1 rating)
Practical defense systems require precise detection during malware outbreaks with only a handful of available samples. Sean Park demonstrates how to detect in-the-wild malware samples with a single training sample of a kind, with the help of TensorFlow's flexible architecture in implementing a novel variable-length generative adversarial autoencoder. Read more.
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11:00am11:40am Thursday, October 31, 2019
Location: Grand Ballroom A/B
Shengsheng Huang and Jason Dai detail their experience and insights about building AI to play the FIFA video game using distributed TensorFlow. Read more.
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11:00am11:40am Thursday, October 31, 2019
Location: Grand Ballroom H
Mikhail Szugalew (The Knowledge Society)
When Mikhail Szugalew discovered that the visually impaired face huge navigational challenges with tasks as simple as crossing the street, he decided to do something about it at just the age of 16, using his experience with TensorFlow to develop object-detection models. He highlights his insights, struggles, process, takeaways, and vision for a better future. Read more.
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11:50am12:30pm Thursday, October 31, 2019
Location: Grand Ballroom A/B
Garrett Lander (Manceps), Al Kari (Manceps)
Automated investing has brought an immense amount of stability to the market, but it has also brought predictability. Garrett Lander and Al Kari examine if an adversarial network can game the behavior of automated investors by learning the patterns in market activity to which they are most vulnerable. Read more.
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11:50am12:30pm Thursday, October 31, 2019
Location: Grand Ballroom H
Laxmi Prajapat (Datatonic), William Fletcher (Datatonic)
Many real-world machine learning applications require generative or reductive sampling of data. Laxmi Prajapat and William Fletcher demonstrate sampling techniques applied to training and testing data directly inside the input function using the tf.data API. Read more.
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1:40pm2:20pm Thursday, October 31, 2019
Location: Grand Ballroom A/B
Asif Hasan (Quantiphi), Adam Hammond (Quantiphi)
Asif Hasan and Adam Hammond dive into how TensorFlow and the Cloud Machine Learning Engine (CMLE) helped a healthcare provider develop a solution designed to predict the patient encounters associated with recurrence of cancer. Read more.
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2:30pm3:10pm Thursday, October 31, 2019
Location: Grand Ballroom A/B
Aalok Patwa (Archbishop Mitty High School)
The public health sector is growing rapidly, and with new methods of data collection comes a need for new analyzation methods. Aalok Patwa explains how to use TensorFlow to create a deep learning model that detects, localizes, and segments colon polyps from colonoscopy image and video. You'll gain technical knowledge of TensorFlow, Keras, and ideas for the application of TensorFlow in medicine. Read more.
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2:30pm3:10pm Thursday, October 31, 2019
Location: Grand Ballroom E
Pengchong Jin (Google)
Pengchong Jin walks you through a typical development workflow on GCP for training and deploying an object detector to a self-driving car. He demonstrates how to train the state-of-the-art RetinaNet model fast using Cloud TPUs and scale up the model effectively on Cloud TPU pods. Pengchong also explains how to export a Tensor-RT optimized mode on GPU for inference. Read more.
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4:10pm4:50pm Thursday, October 31, 2019
Location: Grand Ballroom A/B
Ankit Jain (Uber AI Labs), Piero Molino (Uber AI Labs)
Average rating: ****.
(4.50, 2 ratings)
Ankit Jain and Piero Molino detail how to generate better restaurant and dish recommendations in Uber Eats by learning entity embeddings using graph convolutional networks implemented in TensorFlow. Read more.

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