GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models | Paper Citation Record

Observation ccd385dd-2128-4f81-a6d9-75b208ece07e · outbound

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

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models A survey of recent advances in particle filters and remaining challenges for multitarget tracking,

Reference 1

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

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges,

Reference 2

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

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models State-space models for ecological time-series data: Practical model-fitting,

Reference 3

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

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Particle learning for Bayesian semi-parametric stochastic volatility model,

Reference 4

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This paper cites Operational implementa- tion of a hybrid ensemble/4d-Var global data assimilation system at the Met Office,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Operational implementa- tion of a hybrid ensemble/4d-Var global data assimilation system at the Met Office,

Reference 5

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This paper cites A new approach to linear filtering and prediction problems,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models A new approach to linear filtering and prediction problems,

Reference 6

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This paper cites Maximum likelihood estimates of linear dynamic systems,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Maximum likelihood estimates of linear dynamic systems,

Reference 7

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This paper cites The unscented Kalman filter for nonlinear estimation,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models The unscented Kalman filter for nonlinear estimation,

Reference 8

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This paper cites A tutorial on particle filtering and smoothing: Fifteen years later,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models A tutorial on particle filtering and smoothing: Fifteen years later,

Reference 9

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This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 10

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

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Particle filter networks with application to visual localization,

Reference 11

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This paper cites Differentiable Particle Filtering via Entropy-Regularized Optimal Transport.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

Reference 12

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

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Differentiable Particle Filtering without Modifying the Forward Pass

Reference 13

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This paper cites Deterministic nonperiodic flow,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Deterministic nonperiodic flow,

Reference 14

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This paper cites Predictability: A problem partly solved,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Predictability: A problem partly solved,

Reference 15

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This paper cites Learning a sparse polynomial approximation to the transition function of general state-space models,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Learning a sparse polynomial approximation to the transition function of general state-space models,

Reference 16

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This paper cites S¨arkk¨a, Bayesian Filtering and Smoothing.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models S¨arkk¨a, Bayesian Filtering and Smoothing

Reference 17

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Observation 8fc8318f-1e51-469a-8782-c0b986b771f0 · outbound

This paper cites Regression shrinkage and selection via the lasso,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Regression shrinkage and selection via the lasso,

Reference 18

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This paper cites Graphical inference in linear-Gaussian state-space models,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Graphical inference in linear-Gaussian state-space models,

Reference 19

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This paper cites Importance sampling: a review,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Importance sampling: a review,

Reference 20

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This paper cites Particle Markov chain Monte Carlo methods,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Particle Markov chain Monte Carlo methods,

Reference 21

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This paper cites Variational inference: A review for statisticians,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Variational inference: A review for statisticians,

Reference 22

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This paper cites Sparse bayesian estimation of parameters in linear- gaussian state-space models,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Sparse bayesian estimation of parameters in linear- gaussian state-space models,

Reference 23

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

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models An overview of differentiable particle filters for data-adaptive sequential Bayesian inference

Reference 24

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This paper cites Differentiable Bootstrap Particle Filters for Regime-Switching Models.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Differentiable Bootstrap Particle Filters for Regime-Switching Models

Reference 25

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This paper cites Adam: A Method for Stochastic Optimization.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Adam: A Method for Stochastic Optimization

Reference 26

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This paper cites Jasper: An End-to-End Convolutional Neural Acoustic Model.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Jasper: An End-to-End Convolutional Neural Acoustic Model

Reference 27

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Stochastic self-modulation of waves in nonequilibrium media,

Reference 28

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Lotka-volterra population models,

Reference 29

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Compartmental models in epidemiology,

Reference 30

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Symmetry breaking instabilities in dissipative systems. ii,

Reference 31

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Oscillations in chemical systems. iv. limit cycle behavior in a model of a real chemical reaction,

Reference 32

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Graphit: Iterative reweighted l1 algorithm for sparse graph inference in state-space models,

Reference 33

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This paper cites Sparse graphical linear dynamical systems,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Sparse graphical linear dynamical systems,

Reference 34

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Graphical inference in non-markovian linear-gaussian state-space models,

Reference 35

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This paper cites A backpropagation algorithm for inferring disentagled nodal dynamics and connectivity structure of dynamical networks,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models A backpropagation algorithm for inferring disentagled nodal dynamics and connectivity structure of dynamical networks,

Reference 36

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Sparse graphical linear dynamical systems,

Reference 37

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Graph State-Space Models and Latent Relational Inference

Reference 38

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This paper cites Connecting the dots: Identifying network structure via graph signal processing,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Connecting the dots: Identifying network structure via graph signal processing,

Reference 39

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Collective dynamics of ‘small-world’ networks,

Reference 40

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This paper cites Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks

Reference 41

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Proximal splitting methods in signal processing,

Reference 42

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Convergence of stochastic proximal gradient algorithm,

Reference 43

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Observation bea9806a-059c-4db0-b4c6-5d37f604da16 · outbound

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Stochastic approximations and perturbations in forward-backward splitting for monotone operators,

Reference 44

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This paper cites JAX: composable transformations of Python+NumPy programs,.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models JAX: composable transformations of Python+NumPy programs,

Reference 45

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

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Pytorch: An imperative style, high-performance deep learning library,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:56.425700Z

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GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Optimization with sparsity-inducing penalties,

Reference 47

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Observation 97fd1654-e2da-4e37-bee5-7f6bb4b55704 · outbound

This paper cites Auto-Encoding Variational Bayes.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Auto-Encoding Variational Bayes

Reference 48

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Observation 1da99d35-63ac-4947-bb13-5f2ff4a7c6d3 · outbound

This paper cites Kuramoto,Chemical Oscillations, Waves, and Turbulence.

GraphGrad: Efficient Estimation of Sparse Polynomial Representations for General State-Space Models Kuramoto,Chemical Oscillations, Waves, and Turbulence

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:09:56.388071Z

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