Put AI to Work
April 15-18, 2019
New York, NY

Schedule: Models and Methods sessions

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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Forecasting financial time series with deep learning on Azure
Location: Regent
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, Wee Hyong Tok, and Krishna Anumalasetty 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
Natural language processing with deep learning (SOLD OUT)
Location: Clinton
Secondary topics:  Deep Learning and Machine Learning tools, Models and Methods, Text, Language, and Speech
SOLD OUT
Delip Rao (AI Foundation), Brian McMahan (Wells Fargo)
Average rating: ****.
(4.33, 3 ratings)
Delip Rao and Brian McMahan explore 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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9:00am–12:30pm Tuesday, April 16, 2019
Using topological data analysis to understand, build, and improve neural networks
Location: Sutton South
Secondary topics:  Deep Learning and Machine Learning tools, Models and Methods
Gunnar Carlsson (Ayasdi)
Gunnar Carlsson explains how to use topological data analysis to describe the functioning and learning of a neural network in a compact and understandable way—resulting in material speedups in performance (training time and accuracy) and enabling data-type customization of neural network architectures to further boost performance and widen the applicability of the method to all datasets. Read more.
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11:05am–11:45am Wednesday, April 17, 2019
Seeing is deceiving: The rise of fake media and how to fight back
Location: Regent Parlor
Secondary topics:  Computer Vision, Ethics, Privacy, and Security, Models and Methods
Siwei Lyu (University of Albany)
Average rating: *****
(5.00, 1 rating)
Siwei Lyu reviews the evolution of techniques behind the generation of fake media and discusses several projects in digital media forensics for the detection of fake media, with a special focus on recent work on detecting AI-generated fake videos (DeepFakes). Read more.
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11:05am–11:45am Wednesday, April 17, 2019
The unreasonable effectiveness of structure
Location: Grand Ballroom West
Secondary topics:  Models and Methods
Lise Getoor (University of California, Santa Cruz)
Average rating: ****.
(4.50, 4 ratings)
Much of today's data is noisy, incomplete, heterogeneous in nature, and interlinked in a myriad of complex ways. Lise Getoor discusses AI methods that are able to exploit both the inherent uncertainty and the innate structure in a domain. Along the way, Lise explores the benefit of utilizing structure—and the inherent risk of ignoring structure. Read more.
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1:00pm–1:40pm Wednesday, April 17, 2019
Random search and reproducibility for neural architecture search
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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1:00pm–1:40pm Wednesday, April 17, 2019
Learning from multiagent emergent behaviors in a simulated environment
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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1:50pm–2:30pm Wednesday, April 17, 2019
A software accelerator for machine learning
Location: Grand Ballroom West
Secondary topics:  Models and Methods, Platforms and infrastructure
Vinay Rao (RocketML), Santi Adavani (RocketML)
Average rating: *****
(5.00, 2 ratings)
The AI industry needs new software architectures for distributed systems to solve critical problems. Vinay Rao and Santi Adavani explain why software architectures will lead the next generation of machine learning approaches and how RocketML has built logistic regression models on the KDD12 dataset with ~150 million samples on an eight-Intel Xeon-node cluster in under a minute. Read more.
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2:40pm–3:20pm Wednesday, April 17, 2019
The curse of generality: Deep reinforcement learning in the wild
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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4:05pm–4:45pm Wednesday, April 17, 2019
Automatic concept learning
Location: Grand Ballroom West
Haizi Yu (University of Illinois at Urbana-Champaign)
Average rating: **...
(2.67, 3 ratings)
Can an AI learn the laws of music theory from sheet music in the same human-interpretable form as a music theory textbook? How little prior knowledge is needed to do so? Haizi Yu considers questions like these as he walks you through developing a general framework for automatic concept learning. Read more.
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4:55pm–5:35pm Wednesday, April 17, 2019
How to build privacy and security into deep learning models
Location: Grand Ballroom West
Secondary topics:  Ethics, Privacy, and Security, Models and Methods
Yishay Carmiel (IntelligentWire)
Average rating: *****
(5.00, 2 ratings)
In recent years, we've seen tremendous improvements in artificial intelligence, 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. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development. Read more.
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1:00pm–1:40pm Thursday, April 18, 2019
Using AutoML to automate selection of machine learning models and hyperparameters
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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1:00pm–1:40pm Thursday, April 18, 2019
BERT: Pretraining deep bidirectional transformers for language understanding
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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1:50pm–2:30pm Thursday, April 18, 2019
Deep learning for time series data
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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2:40pm–3:20pm Thursday, April 18, 2019
Chargrid: Understanding 2D documents
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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4:05pm–4:45pm Thursday, April 18, 2019
Deep learning for recommender systems, Or How to compare pears with apples
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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4:55pm–5:35pm Thursday, April 18, 2019
Toward automated AI planning in enterprise: Opportunities and challenges
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.
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4:55pm–5:35pm Thursday, April 18, 2019
A reinforcement learning approach to optimizing preference on a social network
Location: Trianon Ballroom
Secondary topics:  Media, Marketing, Advertising, Models and Methods, Reinforcement Learning
Matthew REYES (Technergetics)
Average rating: *....
(1.00, 1 rating)
Matthew Reyes casts consumer decision making within the framework of random utility and outlines a simplified scenario of optimizing preference on a social network to illustrate the steps in a company’s allocation decision, from learning parameters from data to evaluating the consequences of different marketing allocations. Read more.