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

Schedule: Computer Vision sessions

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.
2:40pm3:20pm Wednesday, April 17, 2019
Interacting with AI
Location: Regent Parlor
Matt Zeiler (Clarifai)
At the core of today's problems with image classification and deep learning lies one fundamental truth: most AI systems operate by choosing the path of least resistance, not the path of highest long-term quality. Matt Zeiler discusses Clarifai's approach to closing the loop on AI and the techniques it employs to counter the AI quality regression phenomenon. Read more.
4:55pm5:35pm Wednesday, April 17, 2019
Implementing AI
Location: Trianon Ballroom
Ted Way (Microsoft), Maharshi Patel (Microsoft), Aishani Bhalla (Microsoft)
Deep neural networks (DNNs) have enabled AI breakthroughs, but serving DNNs at scale has been challenging: Fast and cheap? Won’t be accurate. Fast and accurate? Won’t be cheap. Join Ted Way, Maharshi Patel, and Aishani Bhalla to learn how to use Python and TensorFlow to train and deploy computer vision models on Intel FPGAs with Azure Machine Learning and Project Brainwave. Read more.
1:50pm2:30pm Thursday, April 18, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Eric Oermann (Mount Sinai Health System), Katie Link (Allen Institute for Brain Science)
Average rating: *****
(5.00, 1 rating)
There's significant interest in applying deep learning-based solutions to problems in medicine and healthcare. Eric Oermann and Katie Link identify actionable medical problems, recast them as tractable deep learning problems, and discuss techniques to solve them. 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.
2:40pm3:20pm Thursday, April 18, 2019
Implementing AI
Location: Trianon Ballroom
Ryan Mukherjee (JHU/APL), Neil Fendley (JHU/APL)
Average rating: *****
(5.00, 1 rating)
While deep learning has led to many advancements in computer vision, most research has focused on ground-based imagery. Ryan Mukherjee and Neil Fendley offer an overview of functional Map of the World (fMoW), an ImageNet for satellite imagery built to address this issue, and explain how you can attack or defend these deep learning models. Read more.
2:40pm3:20pm Thursday, April 18, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Enhao Gong (Subtle Medical), Greg Zaharchuk (Stanford University)
Clinical radiology currently faces several clinical issues: improving imaging efficiency, reducing risks, and developing higher imaging quality. Enhao Gong and Greg Zaharchuk explain how Subtle Medical's deep learning/AI solution addresses these problems by enabling faster MRI and faster PET and low-dose scans, providing real clinical and financial benefit to hospitals. 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.