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While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling inference-time behavior in a principled manner, e.g.
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Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach
Jarzynski, C · 1997
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Excursions in Statistical Dynamics
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Annealed importance sampling
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The process of structure-based drug design
Anderson, A. C · 2003
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Comparison of resampling schemes for particle filtering
Douc, R. and Cappé, O · 2005
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On the control of an interacting particle estimation of Schrödinger ground states
Rousset, M · 2006
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Equilibrium sampling from nonequilibrium dynamics
Rousset, M. and Stoltz, G · 2006
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Escorted free energy simulations: Improving convergence by reducing dissipation
Vaikuntanathan, S. and Jarzynski, C · 2008
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Ertl, P. and Schuffenhauer, A · 2009
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Markov Processes: Characterization and Convergence
Ethier, S. N. and Kurtz, T. G · 2009
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Stochastic Methods
Gardiner, C · 2009
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Free Energy Computations: A Mathematical Perspective
Lelièvre, T., Rousset, M., and Stoltz, G · 2010
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Extended-connectivity fingerprints
Rogers, D. and Hahn, M · 2010
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Escorted free energy simulations
Vaikuntanathan, S. and Jarzynski, C · 2011
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Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Muresan, S., and Hopkins, A. L · 2012
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Mean Field Simulation for Monte Carlo Integration
Del Moral, P · 2013
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The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo
Hoffman, M. D. and Gelman, A · 2014
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A new optimal transport distance on the space of finite Radon measures
Kondratyev, S., Monsaingeon, L., and Vorotnikov, D · 2016
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An interpolating distance between optimal transport and Fisher–Rao metrics
Chizat, L., Peyré, G., Schmitzer, B., and Vialard, F.-X · 2018
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Optimal entropy-transport problems and a new Hellinger–Kantorovich distance between positive measures
Liero, M., Mielke, A., and Savaré, G · 2018
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A perspective on multi-target drug discovery and design for complex diseases
Ramsay, R. R., Popovic-Nikolic, M. R., Nikolic, K., Uliassi, E., and Bolognesi, M. L · 2018
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Rare event simulation for stochastic dynamics in continuous time
Angeli, L., Grosskinsky, S., Johansen, A. M., and Pizzoferrato, A · 2019
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Pyro: Deep universal probabilistic programming
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P. A., Horsfall, P., and Goodman, N. D · 2019
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Accelerating Langevin sampling with birth-death
Lu, Y., Lu, J., and Nolen, J · 2019
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Interacting particle approximations of Feynman-Kac measures for continuous-time jump processes
Angeli, L · 2020
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Equivariant flows: exact likelihood generative learning for symmetric densities
Köhler, J., Klein, L., and Noé, F · 2020
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Annealed flow transport Monte Carlo
Arbel, M., Matthews, A., and Doucet, A · 2021
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Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings
Eberhardt, J., Santos-Martins, D., Tillack, A. F., and Forli, S · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., and Zitnik, M · 2021
Compositional generative modeling: A single model is not all you need
Du, Y. and Kaelbling, L · 2024
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Path-guided particle-based sampling
Fan, M., Zhou, R., Tian, C., and Qian, X · 2024
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Derivative-free guidance in continuous and discrete diffusion models with soft value-based decoding
Li, X., Zhao, Y., Wang, C., Scalia, G., Eraslan, G., Nair, S., Biancalani, T., Ji, S., Regev, A., Levine, S., et al · 2024
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Sampling in unit time with kernel Fisher-Rao flow
Maurais, A. and Marzouk, Y · 2024
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BNEM: A Boltzmann sampler based on bootstrapped noised energy matching
OuYang, R., Qiang, B., and Hernández-Lobato, J. M · 2024
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Should EBMs model the energy or the score?
Salimans, T. and Ho, J · 2021
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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 · 2021
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Compositional visual generation with composable diffusion models
Liu, N., Li, S., Du, Y., Torralba, A., and Tenenbaum, J. B · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al · 2022
Cited alongside, same era.
Path integral sampler: A stochastic control approach for sampling
Zhang, Q. and Chen, Y · 2022
Cited alongside, same era.
Particle denoising diffusion sampler
Phillips, A., Dau, H.-D., Hutchinson, M. J., De Bortoli, V., Deligiannidis, G., and Doucet, A · 2024
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Improved sampling via learned diffusions
Richter, L. and Berner, J · 2024
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Simple and effective masked diffusion language models
Sahoo, S. S., Arriola, M., Gokaslan, A., Marroquin, E. M., Rush, A. M., Schiff, Y., Chiu, J. T., and Kuleshov, V · 2024
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Liouville flow importance sampler
Tian, Y., Panda, N., and Lin, Y. T · 2024
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Understanding reinforcement learning-based fine-tuning of diffusion models: A tutorial and review
Uehara, M., Zhao, Y., Biancalani, T., and Levine, S · 2024
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Transport meets variational inference: Controlled Monte Carlo diffusions
Vargas, F., Padhy, S., Blessing, D., and Nusken, N · 2024
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Iterated energy-based flow matching for sampling from Boltzmann densities
Woo, D. and Ahn, S · 2024
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Practical and asymptotically exact conditional sampling in diffusion models
Wu, L., Trippe, B., Naesseth, C., Blei, D., and Cunningham, J. P · 2024
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., and Dong, Y · 2024
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Rethinking therapeutic strategies of dual-target drugs: An update on pharmacological small-molecule compounds in cancer
Yang, Y., Mou, Y., Wan, L.-X., Zhu, S., Wang, G., Gao, H., and Liu, B · 2024
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Reprogramming pretrained target-specific diffusion models for dual-target drug design
Zhou, X., Guan, J., Zhang, Y., Peng, X., Wang, L., and Ma, J · 2024
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Neural sampling from Boltzmann densities: Fisher-Rao curves in the Wasserstein geometry
Chemseddine, J., Wald, C., Duong, R., and Steidl, G · 2025
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Sequential controlled Langevin diffusions
Chen, J., Richter, L., Berner, J., Blessing, D., Neumann, G., and Anandkumar, A · 2025
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Generator matching: Generative modeling with arbitrary Markov processes
Holderrieth, P., Havasi, M., Yim, J., Shaul, N., Gat, I., Jaakkola, T., Karrer, B., Chen, R. T., and Lipman, Y · 2025
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Diffusion models as cartoonists! the curious case of high density regions
Karczewski, R., Heinonen, M., and Garg, V · 2025
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Test-time alignment of diffusion models without reward over-optimization
Kim, S., Kim, M., and Park, D · 2025
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A general framework for inference-time scaling and steering of diffusion models
Singhal, R., Horvitz, Z., Teehan, R., Ren, M., Yu, Z., McKeown, K., and Ranganath, R · 2025
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The superposition of diffusion models using the Itô density estimator
Skreta, M., Atanackovic, L., Bose, A. J., Tong, A., and Neklyudov, K · 2025
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Controlled generation with distilled diffusion energy models and sequential Monte Carlo
Thornton, J., Béthune, L., ZHANG, R., Bradley, A., Nakkiran, P., and Zhai, S · 2025
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Inference-time alignment in diffusion models with reward-guided generation: Tutorial and review
Uehara, M., Zhao, Y., Wang, C., Li, X., Regev, A., Levine, S., and Biancalani, T · 2025
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Efficient evolutionary search over chemical space with large language models
Wang, H., Skreta, M., Ser, C.-T., Gao, W., Kong, L., Strieth-Kalthoff, F., Duan, C., Zhuang, Y., Yu, Y., Zhu, Y., et al · 2025
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