Gumbel-Softmax: Difference between revisions
| (2 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
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]. | ||
| Line 21: | Line 21: | ||
==Resources== | ==Resources== | ||
* [https://www.youtube.com/watch?v=JFgXEbgcT7g | * [https://www.youtube.com/watch?v=JFgXEbgcT7g Talk by Eric Jang] | ||
==References== | ==References== | ||
{{reflist|refs= | {{reflist|refs= | ||
<ref name="jang2017gumbel">Jang, E., Gu, S., & Poole, B. (2017). Categorical Reparameterization with Gumbel-Softmax. International Conference on Learning Representations. Retrieved from https:// | <ref name="jang2017gumbel">Jang, E., Gu, S., & Poole, B. (2017). Categorical Reparameterization with Gumbel-Softmax. International Conference on Learning Representations. Retrieved from https://arxiv.org/abs/1611.01144</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://arxiv.org/abs/1611.00712</ref> | |||
}} | }} | ||