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christopher-beckham.github.io
| | sander.ai
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| | More thoughts on diffusion guidance, with a focus on its geometry in the input space.
| | yang-song.net
1.7 parsecs away

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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 ...
| | proceedings.neurips.cc
1.3 parsecs away

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