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christopher-beckham.github.io | ||
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yang-song.net
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| | | | | This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... | |
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angusturner.github.io
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| | | | | Machine Learning and Data Science. | |
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proceedings.neurips.cc
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tiao.io
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| | | An in-depth practical guide to variational encoders from a probabilistic perspective. | ||
