Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Discover opportunities for applied AI
Organizations that successfully apply AI innovate and compete more effectively. How is AI transforming your business?
Be a part of the program—apply to speak by October 16.

Schedule: Models and Methods sessions

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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Location: Green Room
Secondary topics:  Deep Learning and Machine Learning tools, Financial Services, Models and Methods, Temporal data and time-series
Francesca Lazzeri (Microsoft), Wee Hyong Tok (Microsoft), Krishna Anumalasetty (Microsoft)
Francesca Lazzeri will 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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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Location: Midtown Suite
Secondary topics:  Deep Learning and Machine Learning tools, Models and Methods, Text, Language, and Speech
Delip Rao (R7 Speech Science)
Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks. Read more.
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9:00am12:30pm Tuesday, April 16, 2019
Location: Beekman
Secondary topics:  Deep Learning and Machine Learning tools, Models and Methods
Gunnar Carlsson (Ayasdi)
Using Topological Data Analysis, one can describe the functioning and learning of a neural network in a compact and understandable way. This understanding results in material speedups in performance (training time + accuracy) and allows for data-type customization of neural network architectures to further boost performance and widen the applicability of the method to all data sets. Read more.
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11:05am11:45am Wednesday, April 17, 2019
Location: Regent Parlor
Secondary topics:  Computer Vision, Ethics, Privacy, and Security, Models and Methods
Siwei Lyu (University of Albany)
In this talk, I will first briefly review the evolution of techniques behind the generation of fake media, and then introduce several projects I was involved in digital media forensics for detection of fake media, with a special focus on some of our recent works on detecting AI-generated fake videos (DeepFakes). Read more.
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11:05am11:45am Wednesday, April 17, 2019
Location: Grand Ballroom West
Secondary topics:  Models and Methods
Lise Getoor (University of California, Santa Cruz)
Much of today's data is noisy, incomplete, heterogeneous in nature, and interlinked in a myriad of complex ways. In this talk, I will describe AI methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. I will describe both the benefit of utilizing structure and the inherent risk of ignoring structure. Read more.
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1:00pm1:40pm Wednesday, April 17, 2019
Location: Grand Ballroom West
Secondary topics:  Automation in machine learning and AI, Deep Learning and Machine Learning tools, Models and Methods
Ameet Talwalkar (Carnegie Mellon University and Determined AI)
Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. In this talk, we present work which aims to help ground the empirical results in this field. In this talk we propose new NAS baselines. Read more.
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1:00pm1:40pm Wednesday, April 17, 2019
Location: Regent Parlor
Secondary topics:  Data and Data Networks, Models and Methods, Reinforcement Learning
Danny Lange (Unity Technologies)
Join this session to learn how to create artificially intelligent agents that act in the physical world (through sense perception and some mechanism to take physical actions, such as driving a car). Understand how observing emergent behaviors of multiple AI agents in a simulated virtual environment can lead to the most optimal designs and real-world practices. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Location: Grand Ballroom West
Secondary topics:  Models and Methods, Platforms and infrastructure
vinay rao (RocketML Inc), Santi Adavani (Mr)
Growing data sizes are causing Training times to increase 5x the Moore's law. While Hardware innovations are helping, new software architectures for distributed system are needed for AI industry to solve critical problems. RocketML results show that we can build logistic regression models on KDD12 data set with ~150 Million samples on 8 Intel Xeon node cluster in < 1 minute. Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
Location: Grand Ballroom West
Secondary topics:  Automation in machine learning and AI, Models and Methods, Reinforcement Learning, Reliability and Safety
Sanjay Krishnan (University of Chicago)
I use my work over the last few years on building and deploying an RL-based relational query optimizer, a core component of almost every database system, as an exemplary application that highlights some of the under-appreciated challenges in Deep RL practice. Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
Location: Grand Ballroom West
Secondary topics:  Computer Vision, Deep Learning and Machine Learning tools
Bichen Wu (UC Berkeley)
For years we have been designing neural networks manually, but such design flow is extremely inefficient and designed networks are sub-optimal. To address this, we introduce an automated framework for neural network design and optimization. This approach generates superior neural network design and greatly reduces the need for manual efforts. Read more.
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4:55pm5:35pm Wednesday, April 17, 2019
Location: Grand Ballroom West
Secondary topics:  Ethics, Privacy, and Security, Models and Methods
Yishay Carmiel (IntelligentWire)
In recent years, we have seen tremendous improvements in artificial intelligence. The major breakthroughs are due to the advances of neural-based models. However, the more popular these algorithms and techniques get, the more serious the consequences of data and user privacy. These issues will drastically impact the future of AI research. Read more.
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1:00pm1:40pm Thursday, April 18, 2019
Location: Grand Ballroom West
Secondary topics:  AI case studies, Automation in machine learning and AI, Deep Learning and Machine Learning tools
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'll learn how to use auto ML to automate selection of machine learning models and automate tuning of hyper-parameters. Read more.
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1:00pm1:40pm Thursday, April 18, 2019
Location: Regent Parlor
Secondary topics:  Models and Methods, Text, Language, and Speech
Chang Ming-Wei (Google)
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. Read more.
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1:50pm2:30pm Thursday, April 18, 2019
Location: Grand Ballroom West
Secondary topics:  Financial Services, Models and Methods, Temporal data and time-series
Arun Kejariwal (Independent), Ira Cohen (Anodot)
In this talk we shall shares a novel two-step approach for building more reliable prediction models by integrating anomalies in them. Further, we shall walk the audience through how to marry correlation analysis with anomaly detection, discusses how the topics are intertwined, and details the challenges you may encounter based on production data. Read more.
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2:40pm3:20pm Thursday, April 18, 2019
Location: Grand Ballroom West
Secondary topics:  Computer Vision, Models and Methods, Text, Language, and Speech
Anoop Katti (SAP)
We address understanding documents with 2D layout using machine learning. Examples of such documents are invoices, resumes, presentations etc. (in contrast to plain text documents like tweets, articles and reviews). We explore the shortcomings of the existing techniques and discuss a processing pipeline for 2D documents – the chargrid - pioneered by data scientists at SAP Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Location: Grand Ballroom West
Secondary topics:  Media, Marketing, Advertising, Models and Methods, Retail and e-commerce
Marcel Kurovski (inovex GmbH)
Recommender Systems support decision making with personalized suggestions. They have proven useful in e-commerce, entertainment, or social networks. However, sparse data and linear models are a burden. Application of Deep Learning sets new boundaries and constitutes remarkable results. This talk shows its application on vehicle recommendations at Germany's biggest online vehicle market. Read more.
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4:55pm5:35pm Thursday, April 18, 2019
Location: Grand Ballroom West
Secondary topics:  AI in the Enterprise, Models and Methods
Maja Vukovic (IBM)
Existing AI driven automation in enterprises employ ML, NLP and chatbots. There is additional opportunity for AI Planning to drive reasoning about action trajectories to help build automation. I will demo application of AI planning for migration of legacy infrastructure to Cloud, based on real world examples and data, and discusses challenges in adopting AI planning solutions in the enterprise. Read more.
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4:55pm5:35pm Thursday, April 18, 2019
Location: Rendezvous
Secondary topics:  Media, Marketing, Advertising, Models and Methods, Reinforcement Learning
Matthew Reyes (Independent Researcher and Consultant)
This talk considers optimizing preference towards a product on a social network. The model for consumer decision-making is based on the notion of random utility. The contributions of the model are stochastic decisions that will be learned from data, and the inclusion of marketing under the control of individual companies. These contributions enable a reinforcement learning based approach. Read more.