Sep 9–12, 2019

Schedule: Machine Learning sessions

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9:00am - 5:00pm Monday, September 9 & Tuesday, September 10
Location: 111
Robert Schroll (The Data Incubator)
The TensorFlow library provides computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll walks you through TensorFlow's capabilities in Python from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications. Read more.
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9:00am - 5:00pm Monday, September 9 & Tuesday, September 10
Location: 112
Rich Ott (The Data Incubator)
PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Get the knowledge you need to build deep learning models using real-world datasets and PyTorch with Rich Ott. Read more.
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9:00am - 5:00pm Monday, September 9 & Tuesday, September 10
Location: Santa Clara
Wenming Ye (Amazon Web Services), Miro Enev (NVIDIA)
Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev detail a practical next step in DL learning with instructions, demos, and hands-on labs. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: 231
Skyler Thomas (MapR)
The popular open source Kubeflow project is one of the best ways to start doing machine learning and AI on top of Kubernetes. However, Kubeflow is a huge project with dozens of large complex components. Skyler Thomas dives into the Kubeflow components and how they interact with Kubernetes. He explores the machine learning lifecycle from model training to model serving. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 C/D
Ira Cohen (Anodot)
While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores what it takes to manage ML-based products, the cycle of developing ML-based capabilities (or entire products), and the role of the (product) manager in each step of the cycle. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: Almaden Ballroom
Jason Dai (Intel), Yuhao Yang (Intel), Jiao(Jennie) Wang (Intel), Guoqiong Song (Intel)
Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLS Listings, the World Bank, Baosight, and Midea/KUKA. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 E/F
Lukas Biewald (Weights & Biases)
Join Lukas Biewald to build and deploy long short-term memories (LSTMs), grated recurrent units (GRUs), and other text classification techniques using Keras and scikit-learn. Read more.
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9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 A/B
Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee), Mars Geldard (University of Tasmania)
Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Tim Nugent, and Mars Geldard teach you how to use solution-driven ML AI problem solving with a game engine. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: 231
Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend)
Boris Lublinsky and Chaoran Yu examine ML use in streaming data pipelines, how to do periodic model retraining, and low-latency scoring in live streams. Learn about Kafka as the data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, metadata tracking, and more. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: LL21 C/D
Mo Patel (Independent)
PyTorch captured the minds of ML researchers and developers upon its arrival. Now it's matured into a production-ready ML framework with use cases and applications. Mo Patel explores the PyTorch lifecycle via hands-on examples such as image and text classification and linear modeling. You'll cover other aspects of ML such as transfer learning, data modeling, and deploying to production in labs. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: 230 B
Robert Nishihara (UC Berkeley), Philipp Moritz (University of California, Berkeley), Ion Stoica (UC Berkeley)
Building AI applications is hard, and building the next generation of AI applications, such as online and reinforcement learning (RL), is more challenging. Robert Nishihara, Philipp Moritz, and Ion Stoica lead a deep dive into Ray—a general-purpose framework for programming your cluster—its API, and system architecture and examine application examples, including state-of-the-art algorithms. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: LL21 A/B
Neil Conway (Determined AI), Yoav Zimmerman (Determined AI)
Success with DL requires more than just TensorFlow or Keras. Neil Conway and Yoav Zimmerman detail a range of practical problems faced by DL practitioners and the software tools and techniques you'll need to address the problems, including data prep and augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile and edge optimization. Read more.
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1:30pm5:00pm Tuesday, September 10, 2019
Location: LL21 E/F
Joel Grus (Allen Institute for Artificial Intelligence)
AllenNLP is a PyTorch-based library designed to make it easy to do high-quality research in natural language processing (NLP). Joel Grus explains what modern neural NLP looks like, and you'll get your hands dirty training some models, writing some code, and learning how you can apply these techniques to your own datasets and problems. Read more.
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10:15am10:30am Wednesday, September 11, 2019
Location: Hall 2
Srinivas Narayanan (Facebook)
Srinivas Narayanan takes you beyond fully supervised learning techniques, the next change you're seeing in AI. Read more.
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11:05am11:45am Wednesday, September 11, 2019
Location: Expo Hall 3
Huaixiu Zheng (Uber)
Uber applies natural language processing (NLP) and conversational AI in a number of business domains. Huaixiu Zheng details how Uber applies deep learning in the domain of NLP and conversational AI. You'll learn how Uber implements AI solutions in a real-world environment, as well as cutting-edge research in end-to-end dialogue systems. Read more.
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11:05am11:45am Wednesday, September 11, 2019
Location: 230 B
Enhao Gong (Subtle Medical), Greg Zaharchuk (Stanford University)
Enhao Gong and Greg Zaharchuk detail AI solutions, cleared by the FDA and powered by industry framework, that deliver 4x–10x faster MRI scans, 4x faster PET scans, and up to 10x dosage reduction. Clinical evaluation at hospitals such as Hoag Hospital, UCSF, and Stanford demonstrates the significant and immediate values of AI to improve the productivity of healthcare workflow. Read more.
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11:05am11:45am Wednesday, September 11, 2019
Location: 230 A
Jasjeet Thind (Zillow)
Advances in AI and deep learning enable new technologies to mimic how the human brain interprets scenes, objects, and images, which has major implications for businesses that need to extract meaning from overwhelming quantities of unstructured data. Jasjeet Thind walks you through how implementing computer vision based in deep neural networks allows machines to see images in an entirely new way. Read more.
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11:05am11:45am Wednesday, September 11, 2019
Location: LL21 C/D
Artificial intelligence has revolutionized the way we live, work, and play. With the help of AI, electronic payments have become more secure and more convenient for consumers globally—regardless of currency or form factor. Chiranjeet Chetia and Carolina Barcenas explore a use case in which data and deep learning converge to root out malicious actors and make the payments ecosystem more secure. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: LL21 E/F
Huaiyu Zhu (IBM Research - Almaden), Dulce Ponceleon (IBM Research - Almaden), Yunyao Li (IBM Research - Almaden)
Natural language understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages, but the questions of how to expand these resources without duplicating effort and if it's possible to develop language-agnostic NLU-dependent applications remains. Huaiyu Zhu, Dulce Ponceleon, and Yunyao Li believe the answer is yes. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: 230 C
Every NLP-based document-processing solution depends on converting documents or images to machine-readable text using an OCR solution, but accuracy is limited by the quality of the images. Nagendra Shishodia, Solmaz Torabi, and Chaithanya Manda examine how generative adversarial networks (GANs) can bring significant efficiencies by enhancing resolution and denoising scanned images. Read more.
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11:55am12:35pm Wednesday, September 11, 2019
Location: 230 A
Joy Rimchala (Intuit), TJ Torres (Intuit), Xiao Xiao (Intuit), Hui Wang (Intuit)
Document understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of data scientists, Joy Rimchala, TJ Torres, Xiao Xiao, and Hui Wang, detail the design and modeling methodologies used to build this platform as a service. Read more.
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1:45pm2:25pm Wednesday, September 11, 2019
Location: LL21 A/B
Yi Zhang (Rulai | University of California, Santa Cruz)
Consumers want everything now, at their fingertips, with very little effort. To meet these demands and compete, companies need to fundamentally rethink how they operate. Yi Zhang explores some predictions on how conversational technology will evolve from its current state in 2019. She outlines some common misunderstandings about the technologies and provides case studies from several industries. Read more.
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1:45pm2:25pm Wednesday, September 11, 2019
Location: LL21 C/D
Roshan Sumbaly (Facebook)
There aren't many systems in the world that need to run hundreds of computer vision models (from classification to segmentation) on billions of visual entities (images, videos, 3-D) daily. Roshan Sumbaly walks you through the challenges faced while building such a platform and how, surprisingly, a lot of the answers were found in traditional software engineering best practices. Read more.
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1:45pm2:25pm Wednesday, September 11, 2019
Location: 230 A
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Sequence to sequence (S2S) modeling using neural networks is increasingly becoming mainstream. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for the aforementioned use cases, visualization, real-time anomaly detection, and forecasting. Read more.
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1:45pm2:25pm Wednesday, September 11, 2019
Location: 230 C
Akhilesh Kumar (Adobe)
Photographic defects such as noise, exposure, and blur can ruin the perfect shot. Adobe has developed a solution based on GAN that can identify the defective region images and fix it. Akhilesh Kumar explores how this solution, which can also be applied to fix videos, is better than traditional algorithms and means you won't have to spend hours manually editing the images. Read more.
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1:45pm2:25pm Wednesday, September 11, 2019
Location: LL21 E/F
Vadim Pinskiy (Nanotronics)
Statistical manufacturing has remained largely unchanged since postwar Japan. AI and DL allow for nonlinear feedback and feed-forward systems to be integrated for real-time monitoring and evolution of each part assembly. Vadim Pinskiy explores a system capable of detecting, classifying, and automatically correcting for manufacturing defects in a multinodal process. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: 230 A
Mark Weber (MIT-IBM Watson AI Lab)
Organized crime inflicts human suffering on a massive scale: upward of 700,000 people per year are "exported" in a $40 billion human-trafficking industry enslaving an estimated 40 million people. Such nefarious industries rely on sophisticated money-laundering schemes to operate. Mark Weber explores how a new field of AI called graph convolutional networks can help. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: 230 C
Wei Cai (Cox Communications)
Real-time traffic volume prediction is vital in proactive network management, and many forecasting models have been proposed to address this. However, most are unable to fully use the information in traffic data to generate efficient and accurate traffic predictions for a longer term. Wei Cai explores predicting multistep, real-time traffic volume using many-to-one LSTM and many-to-many LSTM. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: Expo Hall 3
Joseph Spisak (Facebook), Hao Lu (Facebook)
Learn how PyTorch is being used to help accelerate the path from novel research to large-scale production deployment in computer vision, natural language processing, and machine translation at Facebook with Joseph Spisak and Hao Lu. Read more.
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2:35pm3:15pm Wednesday, September 11, 2019
Location: LL21 A/B
Peter Bailis (Sisu Data | Stanford University)
Despite a meteoric rise in data volumes within modern enterprises, enabling nontechnical users to put this data to work in diagnostic and predictive tasks remains a fundamental challenge. Peter Bailis details the lessons learned in building new systems to help users leverage the data at their disposal, drawing on production experience from Facebook, Microsoft, and the Stanford DAWN project. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: LL21 C/D
Ashish Bansal (Twitter)
Twitter has amazing and unique content generated at an enormous velocity internationally in multiple languages. Ashish Bansal provides you with insight into the unique recommendation system challenges at Twitter’s scale and what makes this a fun and challenging task. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 C
Li Erran Li (Scale AI | Columbia University)
Tremendous progress has been made in applying machine learning to autonomous driving. Li Erran Li explores recent advances in applying machine learning to solving the perception, prediction, planning, and control problems of autonomous driving as well as some key research challenges. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 B
Dexter Hadley (University of California, San Francisco)
Typically, large healthcare institutions have large-scale quantities of clinical data to facilitate precision medicine through an AI paradigm. However, this hardly translates into improved care. Dexter Hadley details how UCSF uses NLP to curate clinical data for over 1M mammograms and how deep learning, blockchain, and other approaches translates this into precision oncology. Read more.
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4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 A
Stacy Ashworth (SelectData), Alberto Andreotti (John Snow Labs)
Much business data is still scanned or snapped documents, which is challenging. Stacy Ashworth and Alberto Andreotti explore a real-world case on reading, understanding, classifying, and acting on facts extracted from such image files using state-of-the-art, open source, deep learning-based optical character recognition (OCR), natural language processing (NLP), and forecasting libraries at scale. Read more.
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4:50pm5:30pm Wednesday, September 11, 2019
Location: 230 A
Anuradha Gali (Uber)
There are 15 million trips a day on the Uber platform. Anu Gali walks you through how Uber leverages AI to automate its business model via its unique platform. You'll learn about technology that evolves based on current market insights and dynamically adjusts for the future. She shares best practices and the architecture that enables organizations like Uber to grow and scale rapidly. Read more.
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4:50pm5:30pm Wednesday, September 11, 2019
Location: Expo Hall 3
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. Siddha Ganju and Meher Kasam examine optimizing deep neural nets to run efficiently on mobile devices. Read more.
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4:50pm5:30pm Wednesday, September 11, 2019
Location: 230 C
Sijun He (Twitter), Ali Mollahosseini (Twitter)
Twitter is what’s happening in the world right now. To connect users with the best content, Twitter needs to build up a deep understanding of its noisy and temporal text content. Sijun He provides an overview of the named entity recognition (NER) system at Twitter and explores the challenges Twitter faces to build and scale a large-scale deep learning system to annotate 500 million tweets per day. Read more.
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9:05am9:20am Thursday, September 12, 2019
Location: Hall 2
Michael Jordan (UC Berkeley)
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Michael Jordan details the aim to blend gradient-based methodology with game-theoretic goals as part of a large "microeconomics meets machine learning" program. Read more.
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9:40am9:55am Thursday, September 12, 2019
Location: Hall 2
Sahika Genc (Amazon)
Sahika Genc dives deep into the current state-of-the-art techniques in deep reinforcement learning (DRL) for a variety of use cases. Read more.
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10:10am10:30am Thursday, September 12, 2019
Location: Hall 2
Kenneth Stanley (Uber AI Labs | University of Central Florida)
We think a lot in machine learning about encouraging computers to solve problems, but there's another kind of learning, called open-endedness, that's just beginning to attract attention in the field. Kenneth Stanley walks you through how open-ended algorithms keep on inventing new and ever-more complex tasks and solving them continually—even endlessly. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: Expo Hall 3
Francesca Lazzeri (Microsoft)
Automated machine learning (AutoML) enables data scientists and domain experts to be productive and efficient. AutoML is seen as a fundamental shift in the way in which organizations can approach machine learning. Francesca Lazzeri outlines how to use AutoML to automate machine learning model selection and automate hyperparameter tuning. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: 230 A
Mathew Salvaris (Microsoft), Angus Taylor (Microsoft)
Join Danielle Dean and Wee Hyong Tok to learn best practices and reference architectures (which have been validated in real-world AI/ML projects for customers globally) for implementing AI. Wee Hyong and Danielle detail lessons distilled from working with large global customers on AI/ML projects and the challenges that they overcame. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: LL21 E/F
Chaitanya Shivade (IBM Research)
Using deep learning models to perform natural language inference (NLI) is a fundamental task in natural language processing. Chaitanya Shivade introduces a recently released dataset, MedNLI, for this task in the clinical domain, describes state-of-the-art models, explores how to adapt these into the healthcare domain, and details applications that can leverage these models. Read more.
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11:05am11:45am Thursday, September 12, 2019
Location: 230 B
Danny Lange (Unity Technologies)
This year, Unity introduced Obstacle Tower, a procedurally generated game environment designed to test the capabilities of AI-trained agents. Then, they invited the public to try to solve the challenge. Danny Lange reveals what Unity learned from the contest and the real-world impact of observing the behaviors of multiple AI agents in a simulated virtual environment. Read more.
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11:55am12:35pm Thursday, September 12, 2019
Location: 230 B
Madhura Dudhgaonkar details lessons learned from productizing enterprise ML services across vision, language, recommendations, and anomaly detection over the last 5+ years. You'll walk away with an actionable framework to bootstrap and scale a machine learning function via a real product journey, involving deep learning that was productized in record speed, in spite of having no dataset. Read more.
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11:55am12:35pm Thursday, September 12, 2019
Location: 230 A
Maithra Raghu (Cornell University/Google Brain)
With the fundamental breakthroughs in artificial intelligence and the significant increase of digital health data, there's been enormous interest in AI for healthcare applications. Maithra Raghu examines how to more effectively develop AI algorithms for these settings and the novel prediction challenges and successes arising from the interaction of AI algorithms and human experts. Read more.
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11:55am12:35pm Thursday, September 12, 2019
Location: LL21 C/D
Ting-Fang Yen (DataVisor)
Ting-Fang Yen details a monitor for production machine learning systems that handle billions of requests daily. The approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. See new tools for detecting undesirable model behaviors early in large-scale online ML systems. Read more.
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11:55am12:35pm Thursday, September 12, 2019
Location: 230 C
Vijay Gabale (Infilect)
Beyond computer games and neural architecture search, practical applications of deep reinforcement learning (DRL) to improve classical classification or detection tasks are few and far between. Vijay Gabale outlines a technique and some experiences of applying DRL on improving the distribution input datasets to achieve state-of-the-art performance, specifically on object-detection tasks. Read more.
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1:45pm2:25pm Thursday, September 12, 2019
Location: 230 A
Vijay Agneeswaran (Publicis Sapient), Abhishek Kumar (Publicis Sapient)
Vijay Agneeswaran and Abhishek Kumar explore multi-label text classification problems, where multiple tags or categories have to be associated with given text or documents. Multi-label text classification occurs in numerous real-world scenarios, for instance, in news categorization and in bioinformatics (such as the gene classification problem). Read more.
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1:45pm2:25pm Thursday, September 12, 2019
Location: LL21 E/F
Holden Karau (Google), Trevor Grant (IBM)
Modeling is easy—productizing models, less so. Distributed training? Forget about it. Say hello to Kubeflow with Holden Karau and Trevor Grant—a system that makes it easy for data scientists to containerize their models to train and serve on Kubernetes. Read more.
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1:45pm2:25pm Thursday, September 12, 2019
Location: 230 C
Shashank Prasanna (Amazon Web Services)
Machine learning involves a lot of experimentation. Data scientists spend days, weeks, or months performing algorithm searches, model architecture searches, hyperparameter searches, etc. Shashank Prasanna breaks down how you can easily run large-scale machine learning experiments using containers, Kubernetes, Amazon ECS, and SageMaker. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: LL21 C/D
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: 230 C
Anusua Trivedi (Microsoft)
Modern machine learning models often significantly benefit from transfer learning. Anusua Trivedi details a study of existing text transfer learning literature. She explores popular machine reading comprehension (MRC) algorithms and evaluates and compares the performance of the transfer learning approach for creating a question answering (QA) system for a book corpus using pretrained MRC models. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: 230 A
Sanji Fernando (Optum)
Sanji Fernando examines his experience building, deploying, and operating a deep learning model that improves hospital revenue cycle management, including business alignment, data preparation, model development, model selection, deployment, and operations. Sanji also details key knowledge and opportunities for improvement with deep learning models in healthcare. Read more.
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2:35pm3:15pm Thursday, September 12, 2019
Location: LL21 E/F
Manasi Vartak (Verta.ai)
Enterprises are investing heavily in integrating AI/ML into their business, and yet it remains challenging to transform these research-oriented initiatives into revenue-driving functions due to a lack of efficient tooling. Manasi Vartak examines key methods that enterprise AI teams can leverage with regard to driving revenue, including A/B testing, data pipelines, and reproducibility. Read more.
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4:00pm4:40pm Thursday, September 12, 2019
Location: 230 A
Shourabh Rawat (Trulia)
Lately, 360-degree images have become ubiquitous in industries from real estate to travel. They enable an immersive experience that benefits consumers but creates a challenge for businesses to direct viewers to the most important parts of the scene. Shourabh Rawat walks you through how to identify and extract engaging static 2-D images using specific algorithms and deep learning methods. Read more.
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4:00pm4:40pm Thursday, September 12, 2019
Location: 230 C
Jisheng Wang (Mist)
Increased complexity and business demands continue to make enterprise network operation more challenging. Jisheng Wang outlines the architecture of the first autonomous network operation solution along with two examples of ML-driven automated actions. He also shares some of his experiences and the lessons he learned applying ML/DL and AI to the development of SaaS-based enterprise solutions. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: LL21 E/F
Jonathan Peck (Algorithmia)
ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: 230 A
Alex (Tianchu) Liang (American Tire Distributors)
Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Tianchu Liang details a warehouse staffing solution deployed in 140 distribution centers, where he implemented a long short-term memory (LSTM) recurrent neural network model to generate staffing-level forecasts and optimize staffing schedules. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: LL21 A/B
Mayank Kejriwal (USC Information Sciences Institute)
Embeddings have emerged as an exciting by-product of the deep neural revolution and now apply universally to images, words, documents, and graphs. Many algorithms only require unlabeled datasets, which are plentiful in businesses. Mayank Kejriwal examines what these embeddings really are and how businesses can use them to bolster their AI strategy. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: 230 C
Ramsundar Janakiraman (Aruba Networks, A HPE Company)
While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in capturing semantics present an opportunity for capturing access semantics to model user behavior. Ram Janakiraman explains how, with strong embeddings as a foundation, behavioral use cases can be mapped to NLP models of choice. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: 230 B
Leslie De Jesus (Wovenware)
Leslie De Jesus examines a machine learning solution enabling the Puerto Rico Science, Technology & Research Trust to identify and classify mosquitoes that may be carrying diseases such as Zika and dengue fever. She outlines the challenges, strategy, and technologies used, the results achieved to date, and the implications of the AI project in helping to address a global threat. Read more.
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4:50pm5:30pm Thursday, September 12, 2019
Location: LL21 C/D
Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (lonely.coffee)
Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a constrained virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use AI problem-solving using game engines. Read more.

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