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)
Average rating: ****.
(4.00, 1 rating)
Pamela Vagata explains how Stripe has applied deep learning techniques to predict fraud from raw behavioral data. Join in to learn how the deep learning model outperforms a feature-engineered model both on predictive performance and in the effort spent on data engineering, model construction, tuning, and maintenance. 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 | Determined AI)
Average rating: *****
(5.00, 1 rating)
Neural architecture search (NAS) is a promising research direction that has the potential to replace expert-designed networks with learned, task-specific architectures. Ameet Talwalkar shares work that aims to help ground the empirical results in this field and proposes 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
Cibele Halasz (Twitter), Satanjeev Banerjee (Twitter)
Average rating: *****
(5.00, 1 rating)
Twitter is a company with massive amounts of data, so it's no wonder that the company applies machine learning in myriad of ways. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has made—from modeling to infrastructure—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)
Average rating: *****
(5.00, 7 ratings)
Join Danny Lange 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). You'll discover 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)
Average rating: *****
(5.00, 6 ratings)
Word embeddings such as word2vec have revolutionized language modeling. Maryam Jahanshahi discusses exponential family embeddings, which apply probabilistic embedding models to other data types. Join in to learn how TapRecruit implemented a dynamic embedding model to understand how tech skill sets have changed over three years. 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)
Average rating: ***..
(3.00, 2 ratings)
Drawing on his work building and deploying an RL-based relational query optimizer, a core component of almost every database system, Sanjay Krishnan highlights some of the underappreciated challenges to implementing deep reinforcement learning. 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 (Walmart Labs), Abhishek Kumar (Publicis Sapient)
Average rating: ***..
(3.00, 1 rating)
Vijay Agneeswaran and Abhishek Kumar offer an overview of capsule networks and explain how they help in handling spatial relationships between objects in an image. They also show how to apply them to text analytics. Vijay and Abhishek then explore an implementation of a recurrent capsule network and benchmark the RCN with capsule networks with dynamic routing on text analytics tasks. 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 for model performance, it can quickly become untenable particularly when data size is short. RNNs can quickly memorize and overfit. Vishal Hawa explains how a combination of RNNs and Bayesian networks (PGM) can improve the 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)
Average rating: ****.
(4.17, 6 ratings)
Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is a fundamental shift in how organizations approach machine learning. Francesca Lazzeri and Wee Hyong Tok demonstrate how to use AutoML to automate the selection of machine learning models and automate tuning of hyperparameters. Read more.
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1:00pm1:40pm Thursday, April 18, 2019
Location: Sutton South
Yulia Zvyagelskaya (Dow Jones), Victor Llorente (Dow Jones)
Average rating: **...
(2.00, 1 rating)
Companies have a strong need for complying with anti-money laundering, antibribery, corruption, and economic sanctions regulation in mitigating third-party risk. Yulia Zvyagelskaya and Victor Llorente highlight how Dow Jones Risk & Compliance 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)
Average rating: *****
(5.00, 4 ratings)
Ming-Wei Chang offers an overview of a new language representation model called BERT (Bidirectional Encoder Representations from Transformers). Unlike recent language representation models, BERT is designed to pretrain 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)
Average rating: ****.
(4.00, 3 ratings)
Arun Kejariwal and Ira Cohen share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail 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)
Label leakage is a pervasive problem in predictive modeling data, and it takes on monstrous proportions at enterprise companies, where the data is populated by diverse business processes, making it hard to distinguish cause from effect. Till Bergmann and Leah McGuire explain how Salesforce—which needs to churn out thousands of customer-specific models for any given use case—tackled this problem. 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)
Average rating: ****.
(4.60, 5 ratings)
Anoop Katti explores the shortcomings of the existing techniques for understanding 2D documents and offers an overview of the Character Grid (Chargrid), a new processing pipeline 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)
Average rating: ****.
(4.00, 2 ratings)
Machine learning models are often susceptible to adversarial deception of their input at test time, which leads to poorer performance. Alina Matyukhina investigates the feasibility of deception in source code attribution techniques in real-world environments and explores attack scenarios on users' identities in open source projects—along with 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)
Average rating: *****
(5.00, 2 ratings)
Recommender systems support decision making with personalized suggestions and have proven useful in ecommerce, entertainment, and social networks. Sparse data and linear models are a burden, but the application of deep learning sets new boundaries and offers remarkable results. Join Marcel Kurovski to explore a use case for 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)
Chakri Cherukuri demonstrates how to apply machine learning techniques in quantitative finance, covering use cases involving both structured and alternative datasets. The focus of the talk will be on promoting reproducible research (through Jupyter notebooks and interactive plots) and interpretable 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)
AI planning offers an opportunity to drive reasoning about action trajectories to help build automation. Maja Vukovic demos an application of AI planning for the migration of legacy infrastructure to the cloud, based on real-world examples and data, and discusses challenges in adopting AI planning solutions in the enterprise. Read more.