Agenda

Deep Learning World Las Vegas 2020

May 31-June 4, 2020 – Caesars Palace, Las Vegas


Click here to view the full 8-track agenda for the five co-located conferences at Machine Learning Week (PAW Business, PAW Financial, PAW Healthcare, PAW Industry 4.0, and Deep Learning World).

TRACK TOPICS – The two tracks of the main two-day conference cover these topics:
Techniques and Results
Large Scale Deployed Deep Learning
TRACK TOPICS – The two tracks of the main two-day conference cover these topics:
Techniques and Results
Large Scale Deployed Deep Learning

Pre-Conference Workshops - Sunday, May 31st, 2020

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one day workshop reviews major big data success stories that have transformed businesses and created new markets. Click workshop title above for the fully detailed description. 

Session description
Leader
Marc Smith
Chief Social Scientist
Connected Action Consulting Group
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Gain experience driving R for predictive modeling across real examples and data sets. Survey the pertinent modeling packages.  Click workshop title above for the fully detailed description. 

Session description
Leader
Robert MuenchenUniversity of Tennessee
Manager of Research Computing Support
University of Tennessee
4:30 pm
End of Sunday Pre-Conference Training Workshops
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Pre-Conference Workshops - Monday, June 1st, 2020

8:30 am
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day session surveys standard and advanced methods for predictive modeling (aka machine learning). Click workshop title above for the fully detailed description. 

Session description
Leader
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning. Click workshop title above for the fully detailed description. 

Session description
Leader
James McCaffreyMicrosoft
Senior Scientist Engineer
Microsoft
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general. Click workshop title above for the fully detailed description. 

Session description
Leader
Clinton BrownleyWhatsApp
Data Scientist
WhatsApp
Pre-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Machine learning improves operations only when its predictive models are deployed, integrated and acted upon – that is, only when you operationalize it.  Click workshop title above for the fully detailed description. 

Session description
Leader
James TaylorDecision Management Solutions
CEO
Decision Management Solutions
4:30 pm
End of Monday Pre-Conference Training Workshops
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Deep Learning World - Las Vegas - Day 1 - Tuesday, June 2nd, 2020

8:00 am
Registration
Networking Breakfast
8:45 am
Eric SiegelPredictive Analytics World
Conference Founder
Predictive Analytics World
8:50 am
MACHINE LEARNING WEEK KEYNOTE
Lessons from: Lyft

In this keynote address, Gil Arditi will cover the areas of machine learning development at Lyft, talk about friction points in the model lifecycle – from prototyping and feature engineering to production deployment – and show how Lyft streamlined this process internally. He will also cover a step-by-step example of a model that was recently developed and taken to production.

Session description
Speaker
Gil ArditiLyft
Product Lead, Machine Learning
Lyft
9:15 am
MACHINE LEARNING WEEK KEYNOTE
Lessons from: Google

As principles purporting to guide the ethical development of Artificial Intelligence proliferate, there are questions on what they actually mean in practice. How are they interpreted? How are they applied? How can engineers and product managers be expected to grapple with questions that have puzzled philosophers since the dawn of civilization, like how to create more equitable and fair outcomes for everyone, and how to understand the impact on society of tools and technologies that haven't even been created yet. To help us understand how Google is wrestling with these questions and more, Jen Gennai, Head of Responsible Innovation at Google, will run through past, present and future learnings and challenges related to the creation and adoption of Google's AI Principles.

Session description
Speaker
Jen GennaiGoogle
Head of Responsible Innovation, Global Affairs
Google
9:40 am
The Session Description will be available shortly.
Session description
10:00 am
Exhibits & Morning Coffee Break
10:30 am
KEYNOTE
Case Study: Standard Cognition

At Standard Cognition we are solving the problem of automated scene understanding for cashierless checkout.  Building and maintaining machine learning systems that can be deployed to hundreds of stores poses many machine learning and engineering challenges.  

While many canonical problems in the deep learning literature have focused on solutions that are comprised of a single network or an ensemble of networks for a fixed dataset, a production system may have multiple, modular models that cascade into each other and are trained and evaluated on moving datasets. Such systems of modular deep neural networks have advantages in sample complexity, development scalability through division of labor, and deployment scalability through reusable and testable intermediate outputs, but come at the cost of managing additional complexity.  

Through ensuring reproducible and shareable data-dependent state, we improve our ability to make continuous progress in arbitrarily complex multi-model systems, especially as Standard Cognition scales in number of developers and amount of data.

Session description
Speaker
Sean HendryxStandard Cognition
Machine Learning Engineer
Standard Cognition
11:15 am
5-minute transition between sessions
11:20 am
Track 1: Techniques and Results
Case Study: Facebook

The vast majority of machine learning is supervised learning. Supervised learning can tell us what will happen, but it can't tell us what to do. Let's call that area of machine learning "reasoning", and it compliments supervised learning to power some of the world's most popular websites. Deep reinforcement learning has shown to be incredibly promising reasoning tool.

This talk will cover a variety of approaches to reasoning, including hand-written rules, black box optimization, multi-armed bandits, and deep reinforcement learning. The talk will also introduce ReAgent, an end-to-end open source platform for reasoning and deep RL, and how Facebook is using it for growth campaigns, ad coupons, infrastructure optimization and novel approaches to recommendations ranking.

Session description
Speaker
Jason GauciFacebook
Engineering Manager
Facebook
Track 2: Large Scale Deployed Deep Learning
Case Study: Walmart Labs

Images are the first point of contact between a customer and an e-commerce website. Hence, a well-curated product catalog plays a key role in customer engagement and purchase decisions. In this talk, we present a Deep Learning based system for evaluating, understanding and optimally selecting product images for customers from a catalog of millions of products. We will outline how the system works and discuss unique challenges related to applying machine learning techniques to real massive image data in the retail industry. We will also discuss some techniques we use at Walmart to counter these challenges.

Session description
Speaker
Chhavi YadavWalmart Labs
Data Scientist
Walmart Labs
12:05 pm
Lunch
1:30 pm
Track 1: Techniques and Results
Case Study: LinkedIn

Applying machine learning and deep learning algorithms includes a lot of steps including but not limited to data processing, feature engineering, hyperparameter tuning, etc. Automating these steps improves exploration of these ML improvements easier and increases productivity of machine learning engineers. In this talk, we will explore challenges and solutions for automating the end-to-end process in the wide search space.

Session description
Speakers
Jun JiaLinkedIn
Senior Staff Software Engineer
LinkedIn
Sandeep JhaLinkedIn
Staff Technical Program Manager
LinkedIn
Track 2: Large Scale Deployed Deep Learning
Case Study: Genesys Telecommunication Labs

Genesys PureCloud supports ~100k users making over 60M API calls every day and the volume of data requires an automated system to detect "insider threat" based on user behavior. Because there are few if any examples of this behavior, we developed an anomaly detection system based on deep-learning: in particular, using Transformer networks to learn the probability of a given API call based on a sequence of previous API calls. We compare the detection capability with simpler models (such as Markov chains) and show how anomaly detection can give real-time threat prediction.

Session description
Speaker
Anthony AlfordGenesys
Lead Software Engineer
Genesys
2:15 pm
The Session Description will be available shortly.
Session description
2:35 pm
5-minute transition between sessions
2:40 pm
Track 1: Techniques and Results
Case Study: Shell

Data Science in general and Deep Learning in particular continue to reshape the future of the Energy sector across various segments. From exploration, development and production to downstream and new energies business, measurable value of digitalization has been observed in both efficiencies and savings. Deep Learning is one of the key underlying enablers for creating competitive advantage. This presentation provides an overview of some of use case applications and lessons learned from establishing a platform that progress ideas to embedded business enablers.

Session description
Speaker
Mohamed SidahmedShell Oil Company
Machine Learning and AI Manager
Shell
Track 2: Large Scale Deployed Deep Learning
Case Study: eBay

This session will talk about eBay’s NLP use cases at scale, and how eBay built a Core NLP platform of libraries and services leveraging state-of-the-art deep learning techniques for the e-commerce domain.

The NLP platform provides a deep understanding of textual data which has unlocked numerous use-cases across eBay. One particular use case covered will be using named entity recognition of item titles to extract product aspects. We'll discuss our experience with transformer embeddings, an architecture that's proved most promising, and our next steps in scaling up and out including multi-lingual use cases.

Session description
Speaker
Kumaresan ManickavelueBay
Sr. Product Manager, NLP
eBay
3:25 pm
Exhibits & Afternoon Break
3:55 pm
Track 1: Techniques and Results
Case study: REI Systems

US is affected by severe natural disasters annually. It is important to estimate the damage quickly in order to respond with adequate measures. Current analytical bottleneck occurs due to manual review of the post-disaster areal imagery. Our goal was to develop an algorithm to detect damaged buildings in satellite images.

We used semantic segmentation techniques to train custom models for buildings detection and further damage assessment. We have split the problem into building localization and roof damage detection. Custom roof damage dataset has been created containing 3,000+ images from hurricane Michael satellite images.

Session description
Speakers
Nikolay SorokinREI Systems
Data Scientist
REI Systems
Zulfiqar AhmedREI Systems
REI Systems
Track 2: Large Scale Deployed Deep Learning
Case Study: Facebook

Large scale distributed training has become an essential element to scaling the productivity for ML engineers. Today, ML models are getting larger and more complex in terms of compute and memory requirements. The amount of data we train on at Facebook is huge. This talk outlines distributed training platform that is used in large scale ranking models across Facebook. In this talk, we will learn about the distributed training platform to support large scale data and model parallelism. The talk will also touch base on how this platform is used to express large scale models (Ads ranking, news feed ranking, search, etc), the system used to train this model, and production considerations. You will also learn about the distributed training support for PyTorch and how we are offering a flexible training platform for ML engineers to increase their productivity at facebook scale.

Session description
Speaker
Manoj Kumar KrishnanFacebook
Software Engineer and Tech Lead
Facebook
4:40 pm
5-minute transition between sessions
4:45 pm
Track 1: Techniques and Results
Case study: DeepScale (a Tesla company)

Generative approaches enable creating numerous scenarios coherent with the reality, as long as the representations are good approximations of the real world. In this talk, I will discuss my work at DeepScale, Facebook, and Purdue in extracting such generative representations from 2D and 3D data for mapping, modeling, and reconstruction of spatial data and urban models; combining computer vision, machine learning, and computational geometry for shape understanding. In the second part of the talk, I will introduce FakeCatcher, a unique system that detects deep fake videos in the wild, with high accuracy.

Session description
Speaker
Ilke DemirDeepScale (a Tesla company)
Senior Research Scientist
DeepScale (a Tesla company)
Track 2: Large Scale Deployed Deep Learning
Case Study: Adobe

The focus of the session will be on how deep learning is helping Adobe redefine its customer experience. The two main areas of focus will be cross-lingual and personalized support.

While classification models for single language have been around for a while, they pose limitations in terms of scalability and consistency when applied to many languages. To solve this problem, we rely on multi-lingual deep learning models. Similarly, personalization of support based on non-contextual information has been tried and tested but deep learning is enabling us combine contextual information with non-contextual information to provide support that is more personalized than before.

Session description
Speaker
Piyush ChandraAdobe Systems
Sr. Product Manager, Conversational Automation
Adobe
5:30 pm
Networking Reception
7:00 pm
End of first Conference Day

Deep Learning World - Las Vegas - Day 2 - Wednesday, June 3rd, 2020

8:00 am
Registration & Networking Breakfast
8:55 am
Track 1: Techniques and Results
Case Study: Samsung

Human behavior analysis is one of the critical elements for autonomous driving. Different from the traditional computer vision problems in the automotive space, human behavior analysis requires temporal inputs. Unlike image recognition, videos are three-dimensional (3D) spatio-temporal signals characterizing both the visual appearance and motion dynamics of the involved humans and objects. Extracting useful spatio-temporal representations is important for all video tasks. This talk will cover Samsung's novel researches of using convolutional networks and recurrent networks for video analysis in action and intention recognition pertaining to human interaction with autonomous vehicles.

Session description
Speaker
Lin SunSamsung
Head of Perception
Samsung
9:30 am
The Session Description will be available shortly.
Session description
9:40 am
The Session Description will be available shortly.
Session description
10:00 am
5-minute transition between sessions
10:05 am
Track 1: Techniques and Results
Case Study: Verizon

Wireless Networks are more complex than ever before, and deep learning is an innovative solution for better network operation and management. In this talk we present a case study where we have explored how deep leaning can be used to audit networks to enable advanced networks quality improvement. Key challenges of deep learning when applied to networks will be discussed as well.

Session description
Speakers
Said SoulhiVerizon
Distinguished Member of the Technical Staff
Verizon
Bryan Larish Ph.D.Verizon
Director of Technology
Verizon
10:50 am
Exhibits & Morning Coffee Break
11:20 am
Track 1: Techniques and Results
Case Study: The Vanguard Group

Marketing leads are exposed numerous channels, and it creates complex cross-channel relationship that makes effectiveness of the campaign difficult to comprehend and execute. Are there path-sequences that are better at driving leads than others? Build Bayesian based RNN model and Conduct Path Analysis for marketing campaigns. While RNN provides sequence information , the Bayesian approach makes the approach robust in the presence of noise and uncertainties. The presentation will go on to show how can we visualize the latent space and perform ‘Next best Action’ on the potential leads, thereby maximizing the impact of channel treatments towards any desired outcome.

Session description
Speaker
Vishal HawaThe Vanguard Group
Principal Scientist
The Vanguard Group
12:05 pm
Lunch
1:15 pm
Track 1: Techniques and Results
Case Study: Nike

Enable Data Scientists to own the entire lifecycle of their Model. Using this pattern of Stateless ML Pipelines through Dependency Resolution, pipelines are simplified, reproducibility is achieved and a single CI/CD pipeline can be used for all Modeling use cases. Enable Data Scientists to focus on Model development and automate all the ancillary aspects such as parameter/metric tracking, CI/CD pipelines, scheduling, alarming and reporting.

Through this automation Data Scientists can own all aspects of a Model lifecycle, from Development to Productionization and Operations, all without burdening future development.

Session description
Speaker
James NormanNike
Lead Software Engineer
Nike
2:00 pm
The Session Description will be available shortly.
Session description
2:15 pm
Track 1: Techniques and Results
Case Study: eBay Corporation

A major drawback of employing DNNs in practical settings is that with millions of parameters to be trained they require access to a massive amount of data. We use a state-of-the-art technique called Cross-language Transfer Leaning to alleviate the cold-start problem in NLP DL models for predicting best shipping in emerging markets. Although the language, the prediction tasks and the network architectures are quite different across markets, we exhibit how we can utilize the learned features in one network to significantly improve the accuracy score in the target network/market.

Session description
Speaker
Navid ImanieBay
Applied Researcher
eBay
3:00 pm
Exhibits & Afternoon Break
3:30 pm
Track 1: Techniques and Results
Case Study: Verizon

Recent success of sequence based deep learning models on NLP tasks have drawn attention to their application in time series data analysis. At Verizon, we are exploring RNN, LSTM models to solve our business problems involving time series data. Verizon has rich time series data sets on network cell site performance, customer experience and geospatial information. In my talk, I will discuss how we are analyzing these datasets using deep learning techniques to:

  • Improve nationwide customer experience
  • Improve network performance
  • Optimize capital allocation
Session description
Speaker
Shams ZamanVerizon
Principal Data Scientist
Verizon
4:15 pm
5-minute transition between sessions
4:20 pm
Track 1: Techniques and Results
Case Study: Google

Many organizations respond to inquiries, whether internal or external, over text chats or support tickets. Frequently, the answers to these questions can be found in knowledge bases. We’ll discuss how we at Google approached automatically suggesting the most relevant knowledge base articles using dual encoders and deep learning-based natural language processing. We’ll talk through how this fits into the machine learning project lifecycle, with examples of common pitfalls.


Session description
Speaker
Patrick MillerGoogle
Software Engineering Manager
Google
5:05 pm
End of second Conference Day
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Post-Conference Workshops - Thursday, June 4th, 2020

8:30 am
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This one-day session reveals the subtle mistakes analytics practitioners often make when facing a new challenge (the “deadly dozen”), and clearly explains the advanced methods seasoned experts use to avoid those pitfalls and build accurate and reliable models.  Click workshop title above for the fully detailed description.

Session description
Leader
John Elder Ph.D.Elder Research
Founder & Chair
Elder Research
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

Gain the power to extract signals from big data on your own, without relying on data engineers and Hadoop specialists. Click workshop title above for the fully detailed description.

Session description
Leader
James Casaletto
PhD Candidate
UC Santa Cruz Genomics Institute and former Senior Solutions Architect, MapR
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

This workshop dives into the key ensemble approaches, including Bagging, Random Forests, and Stochastic Gradient Boosting. Click workshop title above for the fully detailed description.

Session description
Leader
Dean AbbottSmarterHQ
Co-Founder and Chief Data Scientist
SmarterHQ
Post-Conference Training Workshop

Full-day: 8:30am – 4:30pm

During this workshop, you will gain hands-on experience deploying deep learning on Google’s TPUs (Tensor Processing Units) at this one-day workshop, scheduled the day immediately after the Deep Learning World and Predictive Analytics World two-day conferences.  Click workshop title above for the fully detailed description.

Session description
4:30 pm
End of Post-Conference Training Workshops
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