How Uber Uses Generative Adversarial Networks
4 engineering articles about Generative Adversarial Networks from Uber's engineering team
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The First Uber Science Symposium brought together experts from various fields to discuss advancements in reinforcement learning (RL), natural language processing (NLP), conversational AI, and deep ...
Computer VisionDeep LearningGenerative Adversarial NetworksMachine LearningPyTorchReinforcement LearningSwiftTensorFlow
Mahdi Namazifar, Gokhan Tur, Jeff Clune, John Sears, Rosanne Liu, Xu Ning, Zoubin Ghahramani
17 min read
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The article introduces the Metropolis-Hastings GAN (MH-GAN), a novel approach to enhance Generative Adversarial Networks (GANs) by leveraging the discriminator for improved sample selection.
R. Turner, Jane Hung, Yunus Saatci, Jason Yosinski
11 min read
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The article discusses Bayesian Generative Adversarial Networks (GANs), presenting a practical Bayesian formulation for unsupervised and semi-supervised learning.
1 min read
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The article discusses advancements in image captioning techniques, highlighting the limitations of existing methods and proposing a new framework based on Conditional Generative Adversarial Network...
2 min read
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