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

Schedule: Models and Methods sessions

9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
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.
9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Implementing AI, Models and Methods
Location: Clinton
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.
9:00am12:30pm Tuesday, April 16, 2019
Models and Methods
Location: Sutton South
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.
9:00am12:30pm Tuesday, April 16, 2019
Location: Trianon Ballroom
Garrett Hoffman (StockTwits)
Average rating: *****
(5.00, 5 ratings)
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks. Read more.
1:45pm5:15pm Tuesday, April 16, 2019
Implementing AI
Location: Trianon Ballroom
Bruno Goncalves (Data For Science, Inc)
Average rating: ***..
(3.50, 6 ratings)
Time series are everywhere around us. Understanding them requires taking into account the sequence of values seen in previous steps and even long-term temporal correlations. Join Bruno Gonçalves to learn how to use recurrent neural networks to model and forecast time series and discover the advantages and disadvantages of recurrent neural networks with respect to more traditional approaches. Read more.
11:05am11:45am Wednesday, April 17, 2019
Models and Methods
Location: Grand Ballroom West
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.
11:05am11:45am Wednesday, April 17, 2019
Models and Methods
Location: Regent Parlor
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.
1:00pm1:40pm Wednesday, April 17, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
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.
1:00pm1:40pm Wednesday, April 17, 2019
Machine Learning, Models and Methods
Location: Regent Parlor
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.
1:50pm2:30pm Wednesday, April 17, 2019
Models and Methods
Location: Grand Ballroom West
vinay rao (RocketML Inc), 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.
2:40pm3:20pm Wednesday, April 17, 2019
Implementing AI
Location: Rendezvous
Sumeet Vij (Booz Allen Hamilton), Matt Speck (Booz Allen Hamilton)
Average rating: ***..
(3.50, 2 ratings)
Sumeet Vij and Matt Speck showcase an innovative application of deep learning to power cognitive conversational agents. You'll learn how chatbots can overcome the limitations of limited training datasets by leveraging transfer learning and deep pretrained models for NLP and how machine learning can advance robotic process automation (RPA) from “robotic” to “cognitive” automation. Read more.
2:40pm3:20pm Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
Vijay Agneeswaran (Publicis Sapient), 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.
2:40pm3:20pm Wednesday, April 17, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
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.
4:05pm4:45pm Wednesday, April 17, 2019
Interacting with AI
Location: Regent Parlor
Kevin He (DeepMotion)
Average rating: ****.
(4.00, 2 ratings)
Digital character interaction is hard to fake, whether it’s between two characters, between users and characters, or between a character and its environment. Nevertheless, interaction is central to building immersive XR experiences, robotic simulation, and user-driven entertainment. Kevin He explains how to use physical simulation and machine learning to create interactive character technology. Read more.
4:55pm5:35pm Wednesday, April 17, 2019
Models and Methods
Location: Grand Ballroom West
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.
11:05am11:45am Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South
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.
1:00pm1:40pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Regent Parlor
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.
1:50pm2:30pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Arun Kejariwal (Facebook), 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.
2:40pm3:20pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
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.
2:40pm3:20pm Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South
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.
2:40pm3:20pm Thursday, April 18, 2019
Interacting with AI
Location: Regent Parlor
Behrooz Hashemian (VideaHealth)
Artificial intelligence has shown great potential to revolutionize clinical medicine and healthcare delivery. However, incorporating these algorithms into clinical workflows involves a big challenge: convincing clinicians and regulators to trust a “black box” solution. Behrooz Hashemian explains how he's helping make deep neural networks interpretable to provide evidence for clinical decisions. Read more.
4:05pm4:45pm Thursday, April 18, 2019
Interacting with AI
Location: Rendezvous
Humayun irshad (Figure Eight)
Average rating: **...
(2.00, 1 rating)
Humayun Irshad offers an overview of an active learning framework that uses a crowdsourcing approach to solve parking sign recognition—a real-world problem in transportation and autonomous driving for which a large amount of unlabeled data is available. The solution generates an accurate model, quickly and cost-effectively, despite the unevenness of the data. Read more.
4:05pm4:45pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
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.
4:05pm4:45pm Thursday, April 18, 2019
Implementing AI
Location: Trianon Ballroom
Jaewon Lee (Naver/LINE), Sihyeung Han (Naver/LINE)
Jaewon Lee and Sihyeung Han walk you through implementing a self-trained dialogue model using AutoML and the Chatbot Builder Framework. You'll discover the value of AutoML, which allows you to provide better model, and learn how AutoML can be applied in different areas of NLP, not just for chatbots. Read more.
4:55pm5:35pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
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.
4:55pm5:35pm Thursday, April 18, 2019
Interacting with AI
Location: Regent Parlor
Baohong Sun (Horace Mann School)
Andrew Caosun discusses a framework that unifies hidden Markov models and deep learn algorithms (RNN) with modeling components that consider long-term memory and semantics of music (LSTM and convolution). It takes users' original creations as input, modifies the raw scores, and generates musically appropriate melodies. Read more.
4:55pm5:35pm Thursday, April 18, 2019
Models and Methods
Location: Trianon Ballroom
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.