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Normalizing flows are a class of deep generative models that are especially interesting for modeling probability distributions in physics, where the exact likelihood of flows allows reweighting to known target energy functions and computing unbiased observables.
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
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