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PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
Faulty reward functions in the wild
Universe
#Exploration: A study of count-based exploration for deep reinforcement learning
OpenAI and Microsoft
On the quantitative analysis of decoder-based generative models
A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models
RL²: Fast reinforcement learning via slow reinforcement learning
Variational lossy autoencoder
Extensions and limitations of the neural GPU
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PixelCNN++: Improving the PixelCNN with discretize...
Faulty reward functions in the wild
Universe
#Exploration: A study of count-based exploration f...
OpenAI and Microsoft
On the quantitative analysis of decoder-based gene...
A connection between generative adversarial networ...
RL²: Fast reinforcement learning via slow reinforc...
Variational lossy autoencoder
Extensions and limitations of the neural GPU
Semi-supervised knowledge transfer for deep learni...
Report from the self-organizing conference
Transfer from simulation to real world through lea...
Infrastructure for deep learning
Machine Learning Unconference
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OpenAI technical goals
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