Deep Learning Summit, San Francisco, 2019

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Bridging the gap between the latest technological research advancements and real world applications in business and society

14+

Hours of content

41

Speakers

12

Interviews

Overview

Each year, RE•WORK brings the Deep Learning Summit to San Francisco for the biggest event on the calendar. The 5th annual Deep Learning Summit featured the latest technological advancements and industry applications of deep learning from world-leading experts including Google Brain, OpenAI, Facebook, Uber AI Labs and many more. Presentation topics includes: Applying ML & NLP in Google Ads, Deep Learning for Incremental Object Detection and Visual Dialog, Deep Robotic Learning, Applying Deep Learning to Article Embedding for Fake News Evaluation, and many more.

Featured Presentations

Watch our featured videos below for free. Scroll down to view the full library of presentations from the event and to purchase access to all the recorded sessions.

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Ian Goodfellow

Ian Goodfellow, Research Scientist at Google Brain

At the time of his presentation, Ian was a Senior Staff Research Scientist at Google and gave an insight into some of the latest breakthroughs in GANs. Dubbed the 'Godfather of GANs', who better to get an overview from than Ian? Post discussion, Ian had one of the longest question...
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Human and Artificial Intelligence in Healthcare

Maithra Raghu, Research Scientist at Google Brain

The combination of breakthroughs in AI and Machine Learning and increasing amounts of digitized medical data have generated significant excitement about the potential to automate medical decision making processes. Among these, there are significant opportunities in designing solutions for settings where AI and ML systems can work seamlessly with human...
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Go-Explore: A New Type of Algorithm for Hard-exploration Problems

Jeff Clune, Senior Research Scientist & Founding Member at Uber AI Labs

A grand challenge in reinforcement learning is producing intelligent exploration, especially when rewards are sparse or deceptive. I will present Go-Explore, a new algorithm for such ‘hard exploration problems.’ Go-Explore dramatically improves the state of the art on benchmark hard-exploration problems, enabling previously unsolvable problems to be solved. I will...
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The Myth of the interpretable, Robust, Compact and High Performance Deep Neural Network

Sara Hooker, Artificial Intelligence Resident at Google Brain

Most progress in machine learning has been measured according to gains in test-set accuracy on tasks like image recognition. However, test-set accuracy appears to be poorly correlated with other design objectives such as interpretability, robustness to adversarial attacks or training compact networks that can be used in resource constrained environments....
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Latent Structure in Deep Robotic Learning

Karol Hausman, Research Scientist & PhD Student at Google Brain & University of Southern California

Traditionally, deep reinforcement learning has focused on learning one particular skill in isolation and from scratch. This often leads to repeated efforts of learning the right representation for each skill individually, while it is likely that such representation could be shared between different skills. In contrast, there is some evidence...
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Advancing State-of-the-art Image Recognition with Deep Learning on Hashtags

Yixuan Li, Research Scientist at Facebook AI (Computer Vision Group)

At Facebook everyday hundreds of millions of users interact with billions of visual contents. By understanding what's in an image, our systems can help connect users with the things that matter most to them. To improve our recognition system, I will talk about two main research challenges: how we train...
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Full Presentation Library

Interview with Shalini Ghosh, Director of AI Research at Samsung Research

Shalini Ghosh, Director of AI Research at Samsung Research America

Interview with Thomas Simonini, Deep Learning Engineer at Deep Reinforcement Learning Course

Thomas Simonini, Deep Learning Engineer at Deep Reinforcement Learning Course

Interview with Arun Chaganty, Head of Artificial Intelligence at Eloquent Labs

Arun Chaganty, Head of Artificial Intelligence at Eloquent Labs

Interview with Tommy Moffat, Innovator, The Knowledge Society

Tommy Moffat, Innovator at The Knowledge Society

Interview with Robinson Piramuthu, Chief Scientist of Computer Vision, eBay Inc.

Robinson Piramuthu, Chief Scientist of Computer Vision at eBay Inc.

Interview with Aravind Srinivas, Ph.D. Student, UC Berkeley

Aravind Srinivas, Ph.D. Student at UC Berkeley

Interview with Chelsea Finn, Research Scientist at Google Brain

Chelsea Finn, PhD Student at UC Berkeley

Interview with Jeff Clune, Senior Research Scientist & Founding Member, Uber AI Labs

Jeff Clune, Senior Research Scientist & Founding Member at Uber AI Labs

Interview with Adrien Gaidon, Machine Learning Lead at Toyota Research Institute

Adrien Gaidon, Machine Learning Lead at Toyota Research Institute

Interview with Mark Crowley, Assistant Professor at University of Waterloo

Mark Crowley, Assistant Professor at University of Waterloo

Applying Deep Learning to Article Embedding for Fake News Evaluation

Mike Tamir, Head of Data Science at Uber ATG

Control Algorithms for Imitation Learning from Observation

Peter Stone, Founder & Director of the Learning Agents Research Group at University of Texas

Beyond Supervised Driving

Adrien Gaidon, Machine Learning Lead at Toyota Research Institute

Brand is Beyond Logos – Understanding Visual Brand

Robinson Piramuthu, Chief Scientist of Computer Vision at eBay Inc.

On-device Neural Networks for Natural Language Processing

Zornitsa Kozareva, Manager at Google

Deep Robotic Learning

Sergey Levine, Assistant Professor at UC Berkeley

Probabilistic Deep Ensembles for Predictive Uncertainty Estimation

Balaji Lakshminarayanan, Senior Research Scientist at DeepMind

Recent Advances From OpenAI

Ilya Sutskever, Co-Founder & Research Director at OpenAI

Combining Planning and Learning

Aravind Srinivas, Ph.D. Student at UC Berkeley

Adversarial Machine Learning

Ian Goodfellow, Research Scientist at Google Brain

Learned Video Compression

Lubomir Bourdev, Co-Founder & CEO at WaveOne

Continuous Object Detection for Conversational Vision

Shalini Ghosh, Director of AI Research at Samsung Research America

A Deep Learning Model for Early Prediction of the Diagnosis of Alzheimer Disease from 18F-FDG PET scan of the Brain

Jae Ho Sohn, Radiology Resident at UCSF Medical Center

Applying Multimodal Integrated Learning Behavioral Analysis and Virtual Personalized Assistant to Adaptive Education

Edgar Kalns, Director of SRI-Squirrel AI Joint Collaboration at Squirrel AI

End-to-End Conversational System for Customer Service Application

Manisha Srivastava, Machine Learning Scientist at Amazon Customer Service

Meta-Learning Deep Networks

Chelsea Finn, PhD Student at UC Berkeley

Using AI to Transform Informational Videos and Our Watching Behavior

Manish Gupta, CEO & Co-Founder at VideoKen

Applying Deep Learning To Airbnb Search

Malay Haldar, Machine Learning Engineer at Airbnb

Object-oriented Perception and Control

Tejas Kulkarni, Research Scientist at DeepMind

Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Search

Sudeep Das, Netflix; Aish Fenton, Netflix

Deep Learning for Robotics and Robotics for Deep Learning

Eric Jang, Research Engineer at Google Brain Robotics

Interview with KP Thai, Senior Learning and Data Scientist at Squirrel AI Learning

KP Thai, Senior Learning and Data Scientist at Squirrel AI Learning

5 Lessons for Improving Training Performance

Emily Watkins, Solution Architect at Pure Storage

Interview with David Kriegman, Professor and Computer Science & Engineering at UCSD

David Kriegman, Professor and Computer Science & Engineering at UCSD

Start Learning Today

Online Video Access $895 $716

Limited offer 20% off. Includes access to the following stages: Deep Learning, AI Assistant, Industry Applications, Futurescaping, and Ethics & Social Responsibility. Contact john@re-work.co for access to this stage only.

Access all of the event videos (presentations with slides), report summaries & video interviews listed above.

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