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This tutorial provides an in-depth guide on inference-time guidance and alignment methods for optimizing downstream reward functions in diffusion models.
Bert has a mouth, and it must speak: Bert as a markov random field language model
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Kocsis, L. and C. Szepesvári (2006) · 2006
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Practical massively parallel monte-carlo tree search applied to molecular design
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Some extensions of score matching
Hyvärinen, A. (2007) · 2007
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Multiobjective optimization: Interactive and evolutionary approaches
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A tutorial on particle filtering and smoothing: Fifteen years later
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Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
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Relative entropy policy search
Peters, J., K. Mulling, and Y. Altun (2010) · 2010
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Denoising diffusion implicit models
Song, J., C. Meng, and S. Ermon (2020) · 2010
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
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A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., G. Gordon, and D. Bagnell (2011) · 2011
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A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
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Padel-descriptor: An open source software to calculate molecular descriptors and fingerprints
Yap, C. W. (2011) · 2011
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An overview of the amber biomolecular simulation package
Salomon-Ferrer, R., D. A. Case, and R. C. Walker (2013) · 2013
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Improving autodock vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets
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Human-level control through deep reinforcement learning
Mnih, V., K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. (2015) · 2015
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Rusu, A. A., S. G. Colmenarejo, C. Gulcehre, G. Desjardins, J. Kirkpatrick, R. Pascanu, V. Mnih, K. Kavukcuoglu, and R. Hadsell (2015) · 2015
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Trust region policy optimization
Schulman, J., S. Levine, P. Abbeel, M. Jordan, and P. Moritz (2015) · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., E. Weiss, N. Maheswaranathan, and S. Ganguli (2015) · 2015
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Mastering the game of go with deep neural networks and tree search
Silver, D., A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. (2016) · 2016
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Reinforcement learning with deep energy-based policies
Haarnoja, T., H. Tang, P. Abbeel, and S. Levine (2017) · 2017
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Levin, D. A. and Y. Peres (2017) · 2017
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Bridging the gap between value and policy based reinforcement learning
Nachum, O., M. Norouzi, K. Xu, and D. Schuurmans (2017) · 2017
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Proximal policy optimization algorithms
Schulman, J., F. Wolski, P. Dhariwal, A. Radford, and O. Klimov (2017) · 2017
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Deeply aggrevated: Differentiable imitation learning for sequential prediction
Sun, W., A. Venkatraman, G. J. Gordon, B. Boots, and J. A. Bagnell (2017) · 2017
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Chemts: an efficient python library for de novo molecular generation
Yang, X., J. Zhang, K. Yoshizoe, K. Terayama, and K. Tsuda (2017) · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J. (2018) · 2018
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Introduction to Riemannian manifolds
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Reinforcement learning and control as probabilistic inference: Tutorial and review
Levine, S. (2018) · 2018
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Distilling policy distillation
Czarnecki, W. M., R. Pascanu, S. Osindero, S. Jayakumar, G. Swirszcz, and M. Jaderberg (2019) · 2019
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A theory of regularized markov decision processes
Geist, M., B. Scherrer, and O. Pietquin (2019) · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Kenton, J. D. M.-W. C. and L. K. Toutanova (2019) · 2019
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Cgmh: Constrained sentence generation by metropolis-hastings sampling
Miao, N., H. Zhou, L. Mou, R. Yan, and L. Li (2019) · 2019
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Elements of sequential monte carlo
Naesseth, C. A., F. Lindsten, T. B. Schön, et al. (2019) · 2019
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Neural empirical bayes
Saremi, S. and A. Hyvärinen (2019) · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and S. Ermon (2019) · 2019
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Maximum entropy monte-carlo planning
Xiao, C., R. Huang, J. Mei, D. Schuurmans, and M. Müller (2019) · 2019
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Monte-carlo tree search as regularized policy optimization
Grill, J.-B., F. Altché, Y. Tang, T. Hubert, M. Valko, I. Antonoglou, and R. Munos (2020) · 2020
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Controlled sequential monte carlo
Heng, J., A. N. Bishop, G. Deligiannidis, and A. Doucet (2020) · 2020
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Denoising diffusion probabilistic models
Ho, J., A. Jain, and P. Abbeel (2020) · 2020
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Autonomous molecular design by monte-carlo tree search and rapid evaluations using molecular dynamics simulations
Kajita, S., T. Kinjo, and T. Nishi (2020) · 2020
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De novo protein design by deep network hallucination
Anishchenko, I., S. J. Pellock, T. M. Chidyausiku, T. A. Ramelot, S. Ovchinnikov, J. Hao, K. Bafna, C. Norn, A. Kang, A. K. Bera, et al. (2021) · 2021
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Structured denoising diffusion models in discrete state-spaces
Austin, J., D. D. Johnson, J. Ho, D. Tarlow, and R. Van Den Berg (2021) · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and A. Nichol (2021) · 2021
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Learning and planning in complex action spaces
Hubert, T., J. Schrittwieser, I. Antonoglou, M. Barekatain, S. Schmitt, and D. Silver (2021) · 2021
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Alphadesign: A de novo protein design framework based on alphafold
Jendrusch, M., J. O. Korbel, and S. K. Sadiq (2021) · 2021
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Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome
On distillation of guided diffusion models
Meng, C., R. Rombach, R. Gao, D. Kingma, S. Ermon, J. Ho, and T. Salimans (2023) · 2023
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Controlled decoding from language models
Mudgal, S., J. Lee, H. Ganapathy, Y. Li, T. Wang, Y. Huang, Z. Chen, H.-T. Cheng, M. Collins, T. Strohman, et al. (2023) · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Podell, D., Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rombach (2023) · 2023
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Aligning text-to-image diffusion models with reward backpropagation
Prabhudesai, M., A. Goyal, D. Pathak, and K. Fragkiadaki (2023) · 2023
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Pseudoinverse-guided diffusion models for inverse problems
Song, J., A. Vahdat, M. Mardani, and J. Kautz (2023) · 2023
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Ji, Y., Z. Zhou, H. Liu, and R. V. Davuluri (2021) · 2021
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Machine translation decoding beyond beam search
Leblond, R., J.-B. Alayrac, L. Sifre, M. Pislar, J.-B. Lespiau, I. Antonoglou, K. Simonyan, and O. Vinyals (2021) · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Nakano, R., J. Hilton, S. Balaji, J. Wu, L. Ouyang, C. Kim, C. Hesse, S. Jain, V. Kosaraju, W. Saunders, et al. (2021) · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole (2021) · 2021
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Fudge: Controlled text generation with future discriminators
Yang, K. and D. Klein (2021) · 2021
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Informative rna base embedding for rna structural alignment and clustering by deep representation learning
Akiyama, M. and Y. Sakakibara (2022) · 2022
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Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness
Bachas, S., G. Rakocevic, D. Spencer, A. V. Sastry, R. Haile, J. M. Sutton, G. Kasun, A. Stachyra, J. M. Gutierrez, E. Yassine, et al. (2022) · 2022
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Song, Y., P. Dhariwal, M. Chen, and I. Sutskever (2023) · 2023
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Accelerating diffusion sampling with classifier-based feature distillation
Sun, W., D. Chen, C. Wang, D. Ye, Y. Feng, and C. Chen (2023) · 2023
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Digress: Discrete denoising diffusion for graph generation
Vignac, C., I. Krawczuk, A. Siraudin, B. Wang, V. Cevher, and P. Frossard (2023) · 2023
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De novo design of protein structure and function with rfdiffusion
Watson, J. L., D. Juergens, N. R. Bennett, B. L. Trippe, J. Yim, H. E. Eisenach, W. Ahern, A. J. Borst, R. J. Ragotte, L. F. Milles, et al. (2023) · 2023
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Se (3) diffusion model with application to protein backbone generation
Yim, J., B. L. Trippe, V. De Bortoli, E. Mathieu, A. Doucet, R. Barzilay, and T. Jaakkola (2023) · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L., A. Rao, and M. Agrawala (2023) · 2023
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Accurate structure prediction of biomolecular interactions with alphafold 3
Abramson, J., J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A. J. Ballard, J. Bambrick, et al. (2024) · 2024
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Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization
Ahdritz, G., N. Bouatta, C. Floristean, S. Kadyan, Q. Xia, W. Gerecke, T. J. O’Donnell, D. Berenberg, I. Fisk, N. Zanichelli, et al. (2024) · 2024
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Campbell, A., J. Yim, R. Barzilay, T. Rainforth, and T. Jaakkola (2024) · 2024
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An all-atom protein generative model
Chu, A. E., J. Kim, L. Cheng, G. El Nesr, M. Xu, R. W. Shuai, and P.-S. Huang (2024) · 2024
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Synflownet: Towards molecule design with guaranteed synthesis pathways
Cretu, M., C. Harris, J. Roy, E. Bengio, and P. Liò (2024) · 2024
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Deft: Efficient fine-tuning of diffusion models by learning the generalised h h -transform
Denker, A., F. Vargas, S. Padhy, K. Didi, S. V. Mathis, R. Barbano, V. Dutordoir, E. Mathieu, U. J. Komorowska, and P. Lio (2024) · 2024
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Diffusion posterior sampling for linear inverse problem solving: A filtering perspective
Dou, Z. and Y. Song (2024) · 2024
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Value augmented sampling for language model alignment and personalization
Han, S., I. Shenfeld, A. Srivastava, Y. Kim, and P. Agrawal (2024) · 2024
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Simulating 500 million years of evolution with a language model
Hayes, T., R. Rao, H. Akin, N. J. Sofroniew, D. Oktay, Z. Lin, R. Verkuil, V. Q. Tran, J. Deaton, M. Wiggert, et al. (2024) · 2024
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Efficient evolution of human antibodies from general protein language models
Hie, B. L., V. R. Shanker, D. Xu, T. U. Bruun, P. A. Weidenbacher, S. Tang, W. Wu, J. E. Pak, and P. S. Kim (2024) · 2024
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Args: Alignment as reward-guided search
Khanov, M., J. Burapacheep, and Y. Li (2024) · 2024
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Towards a mathematical theory for consistency training in diffusion models
Li, G., Z. Huang, and Y. Wei (2024) · 2024
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Derivative-free guidance in continuous and discrete diffusion models with soft value-based decoding
Li, X., Y. Zhao, C. Wang, G. Scalia, G. Eraslan, S. Nair, T. Biancalani, A. Regev, S. Levine, and M. Uehara (2024) · 2024
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Unlocking guidance for discrete state-space diffusion and flow models
Nisonoff, H., J. Xiong, S. Allenspach, and J. Listgarten (2024) · 2024
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Particle denoising diffusion sampler
Phillips, A., H.-D. Dau, M. J. Hutchinson, V. De Bortoli, G. Deligiannidis, and A. Doucet (2024) · 2024
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Steering masked discrete diffusion models via discrete denoising posterior prediction
Rector-Brooks, J., M. Hasan, Z. Peng, Z. Quinn, C. Liu, S. Mittal, N. Dziri, M. Bronstein, Y. Bengio, P. Chatterjee, et al. (2024) · 2024
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Ren, Y., H. Chen, G. M. Rotskoff, and L. Ying (2024) · 2024
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Codonbert: a bert-based architecture tailored for codon optimization using the cross-attention mechanism
Ren, Z., L. Jiang, Y. Di, D. Zhang, J. Gong, J. Gong, Q. Jiang, Z. Fu, P. Sun, B. Zhou, et al. (2024) · 2024
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Simple and effective masked diffusion language models
Sahoo, S. S., M. Arriola, Y. Schiff, A. Gokaslan, E. Marroquin, J. T. Chiu, A. Rush, and V. Kuleshov (2024) · 2024
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Designing dna with tunable regulatory activity using discrete diffusion
Sarkar, A., Z. Tang, C. Zhao, and P. Koo (2024) · 2024
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Generative flows on synthetic pathway for drug design
Seo, S., M. Kim, T. Shen, M. Ester, J. Park, S. Ahn, and W. Y. Kim (2024) · 2024
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Simplified and generalized masked diffusion for discrete data
Shi, J., K. Han, Z. Wang, A. Doucet, and M. K. Titsias (2024) · 2024
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Generative ai for designing and validating easily synthesizable and structurally novel antibiotics
Swanson, K., G. Liu, D. B. Catacutan, A. Arnold, J. Zou, and J. M. Stokes (2024) · 2024
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Score-based diffusion models via stochastic differential equations–a technical tutorial
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Understanding reinforcement learning-based fine-tuning of diffusion models: A tutorial and review
Uehara, M., Y. Zhao, T. Biancalani, and S. Levine (2024) · 2024
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Fine-tuning of continuous-time diffusion models as entropy-regularized control
Uehara, M., Y. Zhao, K. Black, E. Hajiramezanali, G. Scalia, N. L. Diamant, A. M. Tseng, T. Biancalani, and S. Levine (2024) · 2024
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Feedback efficient online fine-tuning of diffusion models
Uehara, M., Y. Zhao, K. Black, E. Hajiramezanali, G. Scalia, N. L. Diamant, A. M. Tseng, S. Levine, and T. Biancalani (2024, 21–27 Jul) · 2024
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Bridging model-based optimization and generative modeling via conservative fine-tuning of diffusion models
Uehara, M., Y. Zhao, E. Hajiramezanali, G. Scalia, G. Eraslan, A. Lal, S. Levine, and T. Biancalani (2024) · 2024
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Amortizing intractable inference in diffusion models for vision, language, and control
Venkatraman, S., M. Jain, L. Scimeca, M. Kim, M. Sendera, M. Hasan, L. Rowe, S. Mittal, P. Lemos, E. Bengio, et al. (2024) · 2024
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Wang, C., M. Uehara, Y. He, A. Wang, T. Biancalani, A. Lal, T. Jaakkola, S. Levine, H. Wang, and A. Regev (2024) · 2024
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Dplm-2: A multimodal diffusion protein language model
Wang, X., Z. Zheng, F. Ye, D. Xue, S. Huang, and Q. Gu (2024) · 2024
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Aligning protein generative models with experimental fitness via direct preference optimization
Widatalla, T., R. Rafailov, and B. Hie (2024) · 2024
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Generative artificial intelligence for de novo protein design
Winnifrith, A., C. Outeiral, and B. L. Hie (2024) · 2024
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Practical and asymptotically exact conditional sampling in diffusion models
Wu, L., B. Trippe, C. Naesseth, D. Blei, and J. P. Cunningham (2024) · 2024
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Proteinbench: A holistic evaluation of protein foundation models
Ye, F., Z. Zheng, D. Xue, Y. Shen, L. Wang, Y. Ma, Y. Wang, X. Wang, X. Zhou, and Q. Gu (2024) · 2024
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Reward-directed conditional diffusion: Provable distribution estimation and reward improvement
Yuan, H., K. Huang, C. Ni, M. Chen, and M. Wang (2024) · 2024
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Accelerating best-of-n via speculative rejection
Zhang, R., M. Haider, M. Yin, J. Qiu, M. Wang, P. Bartlett, and A. Zanette (2024) · 2024
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Probabilistic inference in language models via twisted sequential monte carlo
Zhao, S., R. Brekelmans, A. Makhzani, and R. Grosse (2024) · 2024
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Informed correctors for discrete diffusion models
Zhao, Y., J. Shi, L. Mackey, and S. Linderman (2024) · 2024
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Adding conditional control to diffusion models with reinforcement learning
Zhao, Y., M. Uehara, G. Scalia, T. Biancalani, S. Levine, and E. Hajiramezanali (2024) · 2024
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Inference-time scaling for diffusion models beyond scaling denoising steps
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A general framework for inference-time scaling and steering of diffusion models
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Nonequilibrium markov processes conditioned on large deviations
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