Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY

Schedule: Machine Learning sessions

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11:05am11:45am Wednesday, April 17, 2019
Location: Sutton South
Secondary topics:  AI case studies, Financial Services
Pamela Vagata (Stripe)
Explore how Stripe applies deep-learning to user-behavior for fraud detection. This deep-dive will include data-preparation, modeling methods and performance comparisons. 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: Sutton South
Secondary topics:  AI case studies, Media, Marketing, Advertising, Platforms and infrastructure, Text, Language, and Speech
Twitter is a company with massive amounts of data. Thus, it is no wonder that the company applies machine learning in myriad of ways. In this session, we are going to describe, in depth, one of those use cases: Timeline Ranking. From modeling to infrastructure our goal is to share some of the optimizations that this team have made in order to have models that are both expressive and efficient. 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: Sutton South
Secondary topics:  AI in the Enterprise, Text, Language, and Speech
Maryam Jahanshahi (TapRecruit)
Word embeddings such as word2vec have revolutionized language modelling. In this talk I will discuss exponential family embeddings, which apply probabilistic embedding models to other data types. I describe how we implemented a dynamic embedding model to understand how tech skill-sets have changed over 3 years. The key takeaway is that these models can enrich analysis of specialized datasets. 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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2:40pm3:20pm Wednesday, April 17, 2019
Location: Sutton South
Secondary topics:  Models and Methods, Text, Language, and Speech
Vijay Agneeswaran (Publicis Sapient), Abhishek Kumar (Publicis Sapient)
We illustrate how capsule networks can be industrialized: 1. Overview of capsule networks and how they help in handling spatial relationships between objects in an image. We also learn about how they can be applied to text analytics. 2. We show an implementation of recurrent capsule networks. 3. We also benchmark the RCN with capsule networks with dynamic routing on text analytics tasks. 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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11:05am11:45am Thursday, April 18, 2019
Location: Sutton South
Secondary topics:  AI case studies, Financial Services, Models and Methods
vishal hawa (Vanguard)
While Deep Learning has shown significant promise towards model performance, it can quickly become untenable particularly when data size is short. RNNs can quickly memorize and over-fit . The presentation explains how a combination of RNNs and Bayesian Network (PGM) can improvise sequence-modeling behavior of RNNs. 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: Sutton South
Yulia Zvyagelskaya (Dow Jones), Victor Llorente (Dow Jones)
Companies have a strong need for complying with anti-money laundering, anti-bribery, corruption and economic sanctions regulation in mitigating third party risk. Dow Jones Risk & Compliance deliver research tools and services for vetting and investigation to evaluate these risks with more confidence. The presentation highlights how DJ uses deep learning and NLP for efficient compliance solutions. 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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1:50pm2:30pm Thursday, April 18, 2019
Location: Sutton South
Till Bergmann (Salesforce), Leah McGuire (Salesforce)
A problem in predictive modeling data is label leakage. At Enterprise companies such as Salesforce, this problem takes on monstrous proportions as the data is populated by diverse business processes, making it hard to distinguish cause from effect. We will describe how we tackled this problem at Salesforce, where we need to churn out thousands of customer-specific models for any given use case. 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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2:40pm3:20pm Thursday, April 18, 2019
Location: Sutton South
Secondary topics:  AI case studies, Computer Vision, Ethics, Privacy, and Security, Models and Methods, Reinforcement Learning
Alina Matyukhina (Canadian Institute for Cybersecurity)
Machine learning models are often susceptible to adversarial deception of their input at test time, which is leading to a poorer performance. In this session we will investigate the feasibility of deception in source code attribution techniques in real world environment. This session will present attack scenarios on users identity in open-source projects and discuss possible protection methods. 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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4:05pm4:45pm Thursday, April 18, 2019
Location: Sutton South
Secondary topics:  AI case studies, Financial Services, Text, Language, and Speech
Chakri Cherukuri (Bloomberg LP)
In this talk we will see how machine learning and deep learning techniques can be applied in the field of quantitative finance. We will look at a few use-cases in detail and see how machine learning techniques can supplement and sometimes even improve upon already existing statistical models. We will also look at novel visualizations to help us better understand and interpret these models. 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: Sutton South
Secondary topics:  Models and Methods, Reliability and Safety, Temporal data and time-series
Mohammad Mavadati (Affectiva)
According to the CDC, up to 6,000 fatal crashes caused by drowsy drivers annually. Driver alertness monitoring systems will allow us to develop more reliable vehicles and safer roads. In this talk, Affectiva introduces state-of-the-art vision-based DNN techniques for drowsiness (intensity) annotations and modeling, and reveals some of the AI solutions for in-car drowsiness predictions. Read more.