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Particle Filtering (PF) methods are an established class of procedures for performing inference in non-linear state-space models.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Optimal Transport: Old and New , volume 338
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A tutorial on particle filtering and smoothing: Fifteen years later
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Particle filters for continuous likelihood evaluation and maximisation
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Particle approximations of the score and observed information matrix in state space models with application to parameter estimation
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Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M · 2013
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Efficient likelihood evaluation of state-space representations
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Backward simulation methods for Monte Carlo statistical inference
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On disturbance state-space models and the particle marginal Metropolis–Hastings sampler
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A nonparametric ensemble transform method for Bayesian inference
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Fast computation of Wasserstein barycenters
Cuturi, M. and Doucet, A · 2014
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Nonlinear Time Series: Theory, Methods and Applications with R Examples
Douc, R., Moulines, E., and Stoffer, D · 2014
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Auto-encoding variational Bayes
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Black box variational inference for state space models
Archer, E., Park, I. M., Buesing, L., Cunningham, J., and Paninski, L · 2015
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Variational sequential Monte Carlo
Naesseth, C. A., Linderman, S. W., Ranganath, R., and Blei, D. M · 2018
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Deep state space models for time series forecasting
Rangapuram, S. S., Seeger, M. W., Gasthaus, J., Stella, L., Wang, Y., and Januschowski, T · 2018
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Large-scale optimal transport and mapping estimation
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Importance weighted autoencoders
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Stochastic backpropagation through mixture density distributions
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Second-order accurate ensemble transform particle filters
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Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Altschuler, J., Niles-Weed, J., and Rigollet, P · 2017
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Tensor Monte Carlo: particle methods for the GPU era
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Massively scalable Sinkhorn distances via the Nyström method
Altschuler, J., Bach, F., Rudi, A., and Niles-Weed, J · 2019
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Interpolating between optimal transport and MMD using Sinkhorn divergences
Feydy, J., Séjourné, T., Vialard, F.-X., Amari, S.-I., Trouvé, A., and Peyré, G · 2019
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Hirt, M. and Dellaportas, P · 2019
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An Introduction to Sequential Monte Carlo
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How to train your differentiable filter
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Linear time Sinkhorn divergences using positive features
Scetbon, M. and Cuturi, M · 2020
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End-to-end semi-supervised learning for differentiable particle filters
Wen, H., Chen, X., Papagiannis, G., Hu, C., and Li, Y · 2020
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Towards differentiable resampling
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Quantitative stability and error estimates for optimal transport plans
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