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This article makes discrete masked models for the generative modeling of discrete data controllable.
A simple method for displaying the hydropathic character of a protein
Jack Kyte and Russell F. Doolittle · 1982
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Fabrication of novel biomaterials through molecular self-assembly
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Neural discrete representation learning
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Diffusion models beat GANs on image synthesis
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
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G protein-coupled receptors: structure-and function-based drug discovery
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MaskGIT: Masked generative image transformer
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Classifier-free diffusion guidance
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Autoregressive diffusion models
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Protein design: From the aspect of water solubility and stability
Rui Qing, Shilei Hao, Eva Smorodina, David Jin, Arthur Zalevsky, and Shuguang Zhang · 2022
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Simulating 500 million years of evolution with a language model
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Symbolic music generation with non-differentiable rule guided diffusion
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Derivative-free guidance in continuous and discrete diffusion models with soft value-based decoding
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Discrete diffusion modeling by estimating the ratios of the data distribution
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Diffusion posterior sampling for general noisy inverse problems
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Evolutionary-scale prediction of atomic-level protein structure with a language model
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Loss-guided diffusion models for plug-and-play controllable generation
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A survey of controllable text generation using transformer-based pre-trained language models
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Unlocking guidance for discrete state-space diffusion and flow models
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Your absorbing discrete diffusion secretly models the conditional distributions of clean data
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Simple and effective masked diffusion language models
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Design of complicated all- α \alpha protein structures
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Simplified and generalized masked diffusion for discrete data
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