Top 5 Machine Learning Papers in Q2 2017

Here are the most significant machine learning papers from the second quarter of 2017:

1. Deep Reinforcement Learning from Human Preferences

Authors: Christiano et al. Key Contribution: Introduced a method for training reinforcement learning agents using human feedback, making it easier to align AI systems with human values.

2. One Model To Learn Them All

Authors: Kaiser et al. Key Contribution: Demonstrated that a single neural network architecture could be trained to perform multiple tasks across different domains.

3. Learning to Reason: End-to-End Module Networks for Visual Question Answering

Authors: Hu et al. Key Contribution: Introduced a neural module network that could learn to reason about visual questions by decomposing them into sub-tasks.

4. Deep Voice: Real-time Neural Text-to-Speech

Authors: Arik et al. Key Contribution: Developed a real-time neural text-to-speech system that could generate natural-sounding speech.

5. Learning to Discover Efficient Mathematical Identities

Authors: Zaremba & Sutskever Key Contribution: Showed how reinforcement learning could be used to discover mathematical identities and optimize mathematical expressions.

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