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Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions with high flexibility and expressiveness.
High-Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases
Zwanzig, R. W · 1954
Earlier work this paper cites.
Sampling Organizations and Groups of Unequal Sizes
Kish, L · 1965
Earlier work this paper cites.
New Monte Carlo method to compute the free energy of arbitrary solids. Application to the fcc and hcp phases of hard spheres
Frenkel, D. and Ladd, A. J. C · 1984
Earlier work this paper cites.
On the limited memory bfgs method for large scale optimization
Liu, D. C. and Nocedal, J · 1989
Earlier work this paper cites.
Understanding Molecular Simulation: From Algorithms to Applications
Frenkel, D. and Smit, B · 2001
Earlier work this paper cites.
Targeted free energy perturbation
Jarzynski, C · 2002
Earlier work this paper cites.
Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew
Horn, H. W., Swope, W. C., Pitera, J. W., Madura, J. D., Dick, T. J., Hura, G. L., and Head-Gordon, T · 2004
Earlier work this paper cites.
A potential model for the study of ices and amorphous water: TIP4P/Ice
Abascal, J. L. F., Sanz, E., García Fernández, R., and Vega, C · 2005
Earlier work this paper cites.
Statistically optimal analysis of samples from multiple equilibrium states
Shirts, M. R. and Chodera, J. D · 2008
Earlier work this paper cites.
Determination of phase diagrams via computer simulation: Methodology and applications to water, electrolytes and proteins
Vega, C., Sanz, E., Abascal, J. L. F., and Noya, E. G · 2008
Earlier work this paper cites.
Density estimation by dual ascent of the log-likelihood
Tabak, E. G., Vanden-Eijnden, E., et al · 2010
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S · 2015
Earlier work this paper cites.
Normalizing flows on riemannian manifolds
Gemici, M. C., Rezende, D., and Mohamed, S · 2016
Earlier work this paper cites.
Gaussian error linear units (gelus)
Hendrycks, D. and Gimpel, K · 2016
Earlier work this paper cites.
Input convex neural networks
Amos, B., Xu, L., and Kolter, J. Z · 2017
Earlier work this paper cites.
Density estimation using real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
Earlier work this paper cites.
Openmm 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Neural ordinary differential equations
Chen, T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. K · 2018
Earlier work this paper cites.
Neural network renormalization group
Li, S.-H. and Wang, L · 2018
Earlier work this paper cites.
Using reweighting and free energy surface interpolation to predict solid-solid phase diagrams
Schieber, N. P., Dybeck, E. C., and Shirts, M. R · 2018
Earlier work this paper cites.
Flow-based generative models for markov chain monte carlo in lattice field theory
Albergo, M., Kanwar, G., and Shanahan, P · 2019
Earlier work this paper cites.
Reparameterizing distributions on lie groups
Falorsi, L., de Haan, P., Davidson, T. R., and Forré, P · 2019
Earlier work this paper cites.
Neural Importance Sampling
Müller, T., Mcwilliams, B., Rousselle, F., Gross, M., and Novák, J · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H · 2019
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Configurational mapping significantly increases the efficiency of solid-solid phase coexistence calculations via molecular dynamics: Determining the FCC-HCP coexistence line of Lennard-Jones particles
Schieber, N. P. and Shirts, M. R · 2019
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Unified Efficient Thermostat Scheme for the Canonical Ensemble with Holonomic or Isokinetic Constraints via Molecular Dynamics
Zhang, Z., Liu, X., Yan, K., Tuckerman, M. E., and Liu, J · 2019
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Polymorphism in Molecular Crystals
Bernstein, J · 2020
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Smooth normalizing flows
Köhler, J., Krämer, A., and Noé, F · 2021
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Novel Algorithm to Generate Hydrogen-Disordered Ice Structures
Matsumoto, M., Yagasaki, T., and Tanaka, H · 2021
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Implicit-pdf: Non-parametric representation of probability distributions on the rotation manifold
Murphy, K. A., Esteves, C., Jampani, V., Ramalingam, S., and Makadia, A · 2021
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Estimation of thermodynamic observables in lattice field theories with deep generative models
Nicoli, K. A., Anders, C. J., Funcke, L., Hartung, T., Jansen, K., Kessel, P., Nakajima, S., and Stornati, P · 2021
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2021
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Vflow: More expressive generative flows with variational data augmentation
Chen, J., Lu, C., Chenli, B., Zhu, J., and Tian, T · 2020
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Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
Huang, C.-W., Dinh, L., and Courville, A · 2020
Cited alongside, same era.
Some properties of a cauchy family on the sphere derived from the möbius transformations
Kato, S. and McCullagh, P · 2020
Cited alongside, same era.
Equivariant flows: exact likelihood generative learning for symmetric densities
Köhler, J., Klein, L., and Noé, F · 2020
Cited alongside, same era.
Neural manifold ordinary differential equations
Lou, A., Lim, D., Katsman, I., Huang, L., Jiang, Q., Lim, S. N., and De Sa, C. M · 2020
Cited alongside, same era.
Riemannian continuous normalizing flows
Mathieu, E. and Nickel, M · 2020
Cited alongside, same era.
Asymptotically unbiased estimation of physical observables with neural samplers
Nicoli, K. A., Nakajima, S., Strodthoff, N., Samek, W., Müller, K.-R., and Kessel, P · 2020
Cited alongside, same era.
Rezende, D. J. and Racanière, S · 2021
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Targeted Free Energy Perturbation Revisited: Accurate Free Energies from Mapped Reference Potentials
Rizzi, A., Carloni, P., and Parrinello, M · 2021
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Neural mode jump monte carlo
Sbailò, L., Dibak, M., and Noé, F · 2021
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Learning neural generative dynamics for molecular conformation generation
Xu, M., Luo, S., Bengio, Y., Peng, J., and Tang, J · 2021
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Aspects of scaling and scalability for flow-based sampling of lattice QCD
Abbott, R., Albergo, M. S., Botev, A., Boyda, D., Cranmer, K., Hackett, D. C., Matthews, A. G. D. G., Racanière, S., Razavi, A., Rezende, D. J., Romero-López, F., Shanahan, P. E., and Urban, J. M · 2022
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Free energy calculation of crystalline solids using normalizing flows
Ahmad, R. and Cai, W · 2022
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MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Batatia, I., Kovacs, D. P., Simm, G. N. C., Ortner, C., and Csanyi, G · 2022
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2022
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Matching normalizing flows and probability paths on manifolds
Ben-Hamu, H., Cohen, S., Bose, J., Amos, B., Nickel, M., Grover, A., Chen, R. T. Q., and Lipman, Y · 2022
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Efficient and modular implicit differentiation
Blondel, M., Berthet, Q., Cuturi, M., Frostig, R., Hoyer, S., Llinares-López, F., Pedregosa, F., and Vert, J.-P · 2022
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Phase diagram of the TIP4P/Ice water model by enhanced sampling simulations
Bore, S. L., Piaggi, P. M., Car, R., and Paesani, F · 2022
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Learning Mappings between Equilibrium States of Liquid Systems Using Normalizing Flows
Coretti, A., Falkner, S., Geissler, P., and Dellago, C · 2022
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Adaptive Monte Carlo augmented with normalizing flows
Gabrié, M., Rotskoff, G. M., and Vanden-Eijnden, E · 2022
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Skipping the replica exchange ladder with normalizing flows
Invernizzi, M., Krämer, A., Clementi, C., and Noé, F · 2022
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The first-principles phase diagram of monolayer nanoconfined water
Kapil, V., Schran, C., Zen, A., Chen, J., Pickard, C. J., and Michaelides, A · 2022
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Flow annealed importance sampling bootstrap, 2022
Midgley, L. I., Stimper, V., Simm, G. N. C., Schölkopf, B., and Hernández-Lobato, J. M · 2022
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Normalizing flows for atomic solids
Wirnsberger, P., Papamakarios, G., Ibarz, B., Racanière, S., Ballard, A. J., Pritzel, A., and Blundell, C · 2022
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EDGI: Equivariant diffusion for planning with embodied agents
Brehmer, J., Bose, J., Haan, P. D., and Cohen, T · 2023
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Flow-matching: Efficient coarse-graining of molecular dynamics without forces
Köhler, J., Chen, Y., Krämer, A., Clementi, C., and Noé, F · 2023
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Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling, 2023
Liu, Y., Liu, H., Yin, Y., Wang, Y., Chen, B., and Wang, H · 2023
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