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URL title: Approximating Wasserstein distances with PyTorch - Daniel Daza
URL description: Many problems in machine learning deal with the idea of making two probability distributions to be as close as possible. In the simpler case where we only have observed variables $\mathbf{x}$ (say, images of cats) coming from an unknown distribution $p(\mathbf{x})$, we’d like to find a model $q(\mathbf{x}\vert\theta)$ (like a neural network) that is a good approximation of $p(\mathbf{x})$. It can be shown1 that minimizing $\text{KL}(p\Vert q)$ is equivalent to minimizing the negative log-likelihood, which i
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