Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks | Paper Citation Record

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This paper cites A survey of recent advances in particle ﬁlters and remaining challenges for multitarget tracking.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks A survey of recent advances in particle ﬁlters and remaining challenges for multitarget tracking

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This paper cites Particle learning for Bayesian semi-parametric stochastic volatility model.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Particle learning for Bayesian semi-parametric stochastic volatility model

Reference 2

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This paper cites Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges

Reference 3

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This paper cites State-space models for ecological time-series data: Practical model-ﬁ tting.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks State-space models for ecological time-series data: Practical model-ﬁ tting

Reference 4

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This paper cites Operat ional implementation of a hybrid ensemble /4d- Var global data assimilation system at the Met O ﬃce.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Operat ional implementation of a hybrid ensemble /4d- Var global data assimilation system at the Met O ﬃce

Reference 5

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This paper cites Kalman and extended Kalman ﬁlters : Concept, derivation and properties.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Kalman and extended Kalman ﬁlters : Concept, derivation and properties

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This paper cites The unscented Kalman ﬁlter for nonlinear estimation.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks The unscented Kalman ﬁlter for nonlinear estimation

Reference 7

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This paper cites Novel approach to nonlinear and non- Gaussian Bayesian state estimation.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Novel approach to nonlinear and non- Gaussian Bayesian state estimation

Reference 8

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This paper cites Particle ﬁltering.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Particle ﬁltering

Reference 9

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This paper cites A tutorial on part icle ﬁltering and smoothing: Fifteen years later.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks A tutorial on part icle ﬁltering and smoothing: Fifteen years later

Reference 10

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This paper cites Cambridge University Press, 1st edition, 2013.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Cambridge University Press, 1st edition, 2013

Reference 11

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This paper cites Di ﬀerentiable particle ﬁltering via entropy-regularized optimal transport.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Di ﬀerentiable particle ﬁltering via entropy-regularized optimal transport

Reference 12

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This paper cites Differentiable Particle Filtering without Modifying the Forward Pass.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Differentiable Particle Filtering without Modifying the Forward Pass

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This paper cites An overview of differentiable particle filters for data-adaptive sequential Bayesian inference.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks An overview of differentiable particle filters for data-adaptive sequential Bayesian inference

Reference 14

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Differentiable Bootstrap Particle Filters for Regime-Switching Models

Reference 15

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This paper cites Particle ﬁlter networks with application to visual localization.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Particle ﬁlter networks with application to visual localization

Reference 16

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This paper cites Filtering via simulat ion: Auxiliary particle ﬁlters.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Filtering via simulat ion: Auxiliary particle ﬁlters

Reference 17

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This paper cites Elucidating the auxiliary particle ﬁlter via multiple importance sampling [lecture notes].

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Elucidating the auxiliary particle ﬁlter via multiple importance sampling [lecture notes]

Reference 18

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This paper cites Optimized auxili ary particle ﬁlters: adapting mixture proposals via convex optimization.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Optimized auxili ary particle ﬁlters: adapting mixture proposals via convex optimization

Reference 19

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This paper cites Bugallo, and P etar M.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Bugallo, and P etar M

Reference 20

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This paper cites Neural adaptive sequential Monte Carlo.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Neural adaptive sequential Monte Carlo

Reference 21

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks V ariational sequential Monte Carlo

Reference 22

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This paper cites E nd- to-end learning of gaussian mixture proposals using di ﬀerentiable particle ﬁlters and neural networks.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks E nd- to-end learning of gaussian mixture proposals using di ﬀerentiable particle ﬁlters and neural networks

Reference 23

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Improving population monte carlo: Alternative weighting and resampling schemes

Reference 24

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This paper cites Unrolling particles: Unsupervise d learning of sampling distributions.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Unrolling particles: Unsupervise d learning of sampling distributions

Reference 25

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This paper cites Baraniuk, and Santiago Segarra.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Baraniuk, and Santiago Segarra

Reference 26

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Auto-Encoding Variational Bayes

Reference 27

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Towards Differentiable Resampling

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Adam: A Method for Stochastic Optimization

Reference 29

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks On the Variance of the Adaptive Learning Rate and Beyond

Reference 30

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks

Reference 31

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Jasper: An End-to-End Convolutional Neural Acoustic Model

Reference 32

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Adaptive importance sampling: The past, the present, and the future

Reference 33

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Recurrent neural networks: design and applications

Reference 34

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Bidirectional recu rrent neural networks

Reference 35

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Recent Advances in Recurrent Neural Networks

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This paper cites Informer: Beyond eﬃcient transformer for long sequence time-series forecasti ng.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Informer: Beyond eﬃcient transformer for long sequence time-series forecasti ng

Reference 37

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This paper cites Equinox: neural ne tworks in JAX via callable PyTrees and ﬁltered transforma- tions.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Equinox: neural ne tworks in JAX via callable PyTrees and ﬁltered transforma- tions

Reference 38

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks V ariational infere nce with normalizing ﬂows

Reference 39

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Conditional Density Estimation with Bayesian Normalising Flows

Reference 40

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Categorical Reparameterization with Gumbel-Softmax

Reference 41

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks A family of nonpar ametric density estimation algorithms

Reference 42

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This paper cites Pytorch: An imperative style, high-performance deep learning libra ry.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Pytorch: An imperative style, high-performance deep learning libra ry

Reference 43

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This paper cites JAX: composable transforma- tions of Python+NumPy programs, 2018.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks JAX: composable transforma- tions of Python+NumPy programs, 2018

Reference 44

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Unresolved cited work

Reference 45

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This paper cites Neural ordinary diﬀerential equations.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Neural ordinary diﬀerential equations

Reference 46

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This paper cites On Robustness of Neural Ordinary Differential Equations.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks On Robustness of Neural Ordinary Differential Equations

Reference 47

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This paper cites DiffEqFlux.jl - A Julia Library for Neural Differential Equations.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks DiffEqFlux.jl - A Julia Library for Neural Differential Equations

Reference 48

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This paper cites Predictability: A problem partly solv ed.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Predictability: A problem partly solv ed

Reference 49

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This paper cites Gaussian sum partic le ﬁltering.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Gaussian sum partic le ﬁltering

Reference 50

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This paper cites Chemical Oscillations, W aves, and Turbulence.

Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Chemical Oscillations, W aves, and Turbulence

Reference 51

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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks Unresolved cited work

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