Gumbel-Softmax: Difference between revisions
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Gumbel-softmax<ref name="jang2017gumbel"></ref> is a method to differentiably sample from a categorical distribution. | Gumbel-softmax<ref name="jang2017gumbel"></ref><ref name="madison2017concrete"></ref> is a method to differentiably sample from a categorical distribution. | ||
It is available in PyTorch as [https://pytorch.org/docs/stable/generated/torch.nn.functional.gumbel_softmax.html torch.nn.functional.gumbel_softmax]. | It is available in PyTorch as [https://pytorch.org/docs/stable/generated/torch.nn.functional.gumbel_softmax.html torch.nn.functional.gumbel_softmax]. | ||
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<ref name="jang2017gumbel">Jang, E., Gu, S., & Poole, B. (2017). Categorical Reparameterization with Gumbel-Softmax. International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=rkE3y85ee</ref> | <ref name="jang2017gumbel">Jang, E., Gu, S., & Poole, B. (2017). Categorical Reparameterization with Gumbel-Softmax. International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=rkE3y85ee</ref> | ||
<ref name="madison2017concrete">Maddison, C. J., Mnih, A., & Teh, Y. W. (2017). The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. International Conference on Learning Representations. Retrieved from https://openreview.net/forum?id=S1jE5L5gl</ref> | |||
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