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Title: GitHub - Murgio/tensor2tensor: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. · GitHub

Open Graph Title: GitHub - Murgio/tensor2tensor: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

X Title: GitHub - Murgio/tensor2tensor: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

Description: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. - Murgio/tensor2tensor

Open Graph Description: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. - Murgio/tensor2tensor

X Description: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. - Murgio/tensor2tensor

Opengraph URL: https://github.com/Murgio/tensor2tensor

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accelerate ML research	https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html
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	https://github.com/Murgio/tensor2tensor#quick-start
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	https://github.com/Murgio/tensor2tensor#contents
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	https://github.com/Murgio/tensor2tensor#papers
this paper	https://arxiv.org/abs/1803.07416
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Attention Is All You Need	https://arxiv.org/abs/1706.03762
Depthwise Separable Convolutions for Neural Machine Translation	https://arxiv.org/abs/1706.03059
One Model To Learn Them All	https://arxiv.org/abs/1706.05137
Discrete Autoencoders for Sequence Models	https://arxiv.org/abs/1801.09797
Generating Wikipedia by Summarizing Long Sequences	https://arxiv.org/abs/1801.10198
Image Transformer	https://arxiv.org/abs/1802.05751
Training Tips for the Transformer Model	https://arxiv.org/abs/1804.00247
Self-Attention with Relative Position Representations	https://arxiv.org/abs/1803.02155
Fast Decoding in Sequence Models using Discrete Latent Variables	https://arxiv.org/abs/1803.03382
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost	https://arxiv.org/abs/1804.04235
Universal Transformers	https://arxiv.org/abs/1807.03819
Attending to Mathematical Language with Transformers	https://arxiv.org/abs/1812.02825
The Evolved Transformer	https://arxiv.org/abs/1901.11117
Model-Based Reinforcement Learning for Atari	https://arxiv.org/abs/1903.00374
VideoFlow: A Flow-Based Generative Model for Video	https://arxiv.org/abs/1903.01434
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