30 Influential AI Presentations from 2019

1) Secure Deep Reinforcement Learning

2) Building Knowledge For AI Agents With Reinforcement Learning

3) Developing Innovative Data Science Solutions To Increase Customer Retention

4) Generative Adversarial Networks — An Overview of Development

5) Understanding the limitations of AI: When Algorithms Fail

6) Why aren’t our AI Assistants smarter?

7) The Myth of the interpretable, Robust, Compact and High Performance Deep Neural Network

8) 3D Perception of Human Appearance and Geometry in the Wild

9) Few-Shot Learning: Thoughts On Where We Should Be Going

10) Safety First: AI To Detect Distracted Driving

11) AI for Humanity: How AI Could Benefit Us All

12) Generalizing The Lottery Ticket Hypothesis Across Datasets and Optimizers and Beyond Supervised Image Classification

13) Tricks for Deep Learning Implemented at British Petroleum

14) The New Social Contract — Humanizing Artificial Intelligence

15) Machine Learning systems Design

16) Predicting What Drives Human Attention in Photographs, Visualizations, and Graphic Designs

17) How Do You Know What A Deep Network Has Learned?

18) New Optimization Perspectives on Generative Adversarial Networks

19) Rewards, Resets, Exploration: Bottlenecks for Scaling Deep RL in Robotics

20) A Neural Collaborative Filtering Model for Product Size Recommendations in Retail

21) Data Products at Scale: Credit Card Fraud Detection for Over 800,000 Businesses

22) POET: Endlessly Generating Increasingly Complex and Diverse Learning Environments and their Solutions through the Paired Open-Ended Trailblazer

23) Using Deep Learning to Support Customer Operations

24) Deep Learning and Cognition — An Overview of 2019 with Yoshua Bengio

25) Learning How the Genome Folds in 3D

26) Advancing State-of-the-art Image Recognition with Deep Learning on Hashtags

27) Solve for H: Leveraging AI to Solve Problems for Human Kind

28) Human and Artificial Intelligence in Healthcare

29) Collaboratively Building Trust in AI

30) Building Generative Models of Symptomatic Health Data for Autonomous Deep Space Missions

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