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| ▲ | fumeux_fume 5 hours ago | parent | next [-] |
| There are many applications. I mainly see it used for detecting drift in datasets for ML models. It has a nice benefit over the KL divergence in the case where the two distributions you're measuring have no overlap (KL won't compute, but JS will just return 0). Also, when taking its square root you get a distance rather than a divergence which allows you to compare it to JSD measurements of other distributions. |
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| ▲ | patcon 2 hours ago | parent [-] | | > Also, when taking its square root you get a distance Easy conversion into a distance metric is hugely valuable to making the property amenable to KNN-based dimensionality reduction algos (and I'm sure other things I don't understand, as a non-mathematician) Here's a library that the creator of UMAP provides (UMAP being a workhorse of dimensional reduction algos), for doing approx nearest neighbor search:
https://pynndescent.readthedocs.io/en/latest/api.html#pynnde... |
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| ▲ | andy99 5 hours ago | parent | prev | next [-] |
| Iirc (and I could be wrong, this is from memory) JS divergence is what is minimized in GANs (where we simultaneously train a generator and real/synthetic classifier with the goal of each trying to beat the other to converge on real looking synthetic data), at least for some training methods. I don’t think GANs are used much now in comparison to diffusion models, but as recently as a few years ago they were the standard way to make fake data, a la “this face does not exist” |
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| ▲ | 5 hours ago | parent | prev [-] |
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