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Combining discrete and continuous data is an important capability for generative models.
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Fokker–planck equations for a free energy functional or markov process on a graph
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Auto-encoding variational bayes
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Stochastic backpropagation and approximate inference in deep generative models
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Deep unsupervised learning using nonequilibrium thermodynamics
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Stochastic processes: From applications to theory
Del Moral, P. and Penev, S · 2017
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Decoupled weight decay regularization
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Attention is all you need
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Language models are unsupervised multitask learners
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Deep learning in protein structural modeling and design
Gao, W., Mahajan, S. P., Sulam, J., and Gray, J. J · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and Van Den Berg, R · 2021
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Accurate prediction of protein structures and interactions using a three-track neural network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P., and Welling, M · 2021
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A variational perspective on diffusion-based generative models and score matching
Huang, C.-W., Lim, J. H., and Courville, A. C · 2021
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Highly accurate protein structure prediction with alphafold
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High-accuracy protein structure prediction in casp14
Pereira, J., Simpkin, A. J., Hartmann, M. D., Rigden, D. J., Keegan, R. M., and Lupas, A. N · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
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A continuous time framework for discrete denoising models
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Robust deep learning–based protein sequence design using proteinmpnn
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Continuous diffusion for categorical data
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Gong, S., Li, M., Feng, J., Wu, Z., and Kong, L · 2023
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Graves, A., Srivastava, R. K., Atkinson, T., and Gomez, F · 2023
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Protein design with guided discrete diffusion
Gruver, N., Stanton, S., Frey, N. C., Rudner, T. G., Hotzel, I., Lafrance-Vanasse, J., Rajpal, A., Cho, K., and Wilson, A. G · 2023
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Reinforced self-training (rest) for language modeling
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Likelihood-based diffusion language models
Gulrajani, I. and Hashimoto, T. B · 2023
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Elucidating the design space of diffusion-based generative models
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Diffusion-lm improves controllable text generation
Li, X., Thickstun, J., Gulrajani, I., Liang, P. S., and Hashimoto, T. B · 2022
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Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., and Ma, J · 2022
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Concrete score matching: Generalized score matching for discrete data
Meng, C., Choi, K., Song, J., and Ermon, S · 2022
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Categorical sdes with simplex diffusion
Richemond, P. H., Dieleman, S., and Doucet, A · 2022
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Protein sequence and structure co-design with equivariant translation
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Hua, C., Luan, S., Xu, M., Ying, R., Fu, J., Ermon, S., and Precup, D · 2023
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Retrobridge: Modeling retrosynthesis with markov bridges
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Illuminating protein space with a programmable generative model
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Tabddpm: Modelling tabular data with diffusion models
Kotelnikov, A., Baranchuk, D., Rubachev, I., and Babenko, A · 2023
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Lin, Y. and AlQuraishi, M · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al · 2023
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Flow matching for generative modeling
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M · 2023
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Joint generation of protein sequence and structure with rosettafold sequence space diffusion
Lisanza, S. L., Gershon, J. M., Tipps, S. W. K., Arnoldt, L., Hendel, S., Sims, J. N., Li, X., and Baker, D · 2023
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Flow straight and fast: Learning to generate and transfer data with rectified flow
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Discrete diffusion language modeling by estimating the ratios of the data distribution
Lou, A., Meng, C., and Ermon, S · 2023
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Moldiff: Addressing the atom-bond inconsistency problem in 3d molecule diffusion generation
Peng, X., Guan, J., Liu, Q., and Ma, J · 2023
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Sparse training of discrete diffusion models for graph generation
Qin, Y., Vignac, C., and Frossard, P · 2023
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Blackout diffusion: Generative diffusion models in discrete-state spaces
Santos, J. E., Fox, Z. R., Lubbers, N., and Lin, Y. T · 2023
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On kinetic optimal probability paths for generative models
Shaul, N., Chen, R. T., Nickel, M., Le, M., and Lipman, Y · 2023
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Scientific discovery in the age of artificial intelligence
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al · 2023
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De novo design of protein structure and function with rfdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2023
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Restart sampling for improving generative processes
Xu, Y., Deng, M., Cheng, X., Tian, Y., Liu, Z., and Jaakkola, T · 2023
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Fast non-autoregressive inverse folding with discrete diffusion
Yang, J. J., Yim, J., Barzilay, R., and Jaakkola, T · 2023
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Graph denoising diffusion for inverse protein folding
Yi, K., Zhou, B., Shen, Y., Liò, P., and Wang, Y. G · 2023
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Formulating discrete probability flow through optimal transport
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Ma, N., Goldstein, M., Albergo, M. S., Boffi, N. M., Vanden-Eijnden, E., and Xie, S · 2024
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