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More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains one of the most effective methods for marginal likelihood estimation.
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Time reversal of diffusions
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A generalized guided Monte Carlo algorithm
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Logarithmic Sobolev inequalities and Langevin algorithms in ℝ n \mathbb{R}^{n}
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Exponential convergence of Langevin distributions and their discrete approximations
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Nonequilibrium equality for free energy differences
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Nonequilibrium measurements of free energy differences for microscopically reversible Markovian systems
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Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
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Annealed importance sampling
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An auxiliary variational method
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Estimation of non-normalized statistical models by score matching
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Sequential Monte Carlo samplers
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An invitation to sequential Monte Carlo samplers
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Optimal finite-time processes in stochastic thermodynamics
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On the quantitative analysis of deep belief networks
Salakhutdinov, R. and Murray, I. (2008) · 2008
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Application of Girsanov theorem to particle filtering of discretely observed continuous-time non-linear systems
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MCMC using Hamiltonian dynamics
Neal, R. M. (2011) · 2011
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A connection between score matching and denoising autoencoders
Vincent, P. (2011) · 2011
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Nice: Non-linear independent components estimation
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The DeepMind JAX Ecosystem
Babuschkin, I., Baumli, K., Bell, A., Bhupatiraju, S., Bruce, J., Buchlovsky, P., Budden, D., Cai, T., Clark, A., Danihelka, I., Fantacci, C., Godwin, J., Jones, C., Hennigan, T., Hessel, M., Kapturowski, S., Keck, T., Kemaev, I., King, M., Martens, L., Mikulik, V., Norman, T., Quan, J., Papamakarios, G., Ring, R., Ruiz, F., Sanchez, A., Schneider, R., Sezener, E., Spencer, S., Srinivasan, S., Stokowiec, W., and Viola, F. (2020) · 2020
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Controlled sequential Monte Carlo
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Denoising diffusion probabilistic models
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Stochastic normalizing flows
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Annealed flow transport Monte Carlo
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On the simulated annealing in ℝ d \mathbb{R}^{d}
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