Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cox, Benjamin, Segarra, Santiago, Elvira, Victor
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Recovering the state and dynamics of autonomous system with partial states solution using neural networks
by: Kag, Vijay
Published: (2024)

Redesigning graph filter-based GNNs to relax the homophily assumption
by: Rey, Samuel, et al.
Published: (2024)

Auto-differentiable data assimilation: Co-learning of states, dynamics, and filtering algorithms
by: Adrian, Melissa, et al.
Published: (2026)

Sampling with Shielded Langevin Monte Carlo Using Navigation Potentials
by: Zilberstein, Nicolas, et al.
Published: (2025)

Learning the dynamics of a one-dimensional plasma model with graph neural networks
by: Carvalho, Diogo D, et al.
Published: (2023)

Generalized Lanczos method for systematic optimization of neural-network quantum states
by: Wang, Jia-Qi, et al.
Published: (2025)

Uncertainty-preserving deep knowledge tracing with state-space models
by: Christie, S. Thomas, et al.
Published: (2024)

Flexible inference for animal learning rules using neural networks
by: Liu, Yuhan Helena, et al.
Published: (2025)

Input-gradient space particle inference for neural network ensembles
by: Trinh, Trung, et al.
Published: (2023)

Calibration of a neural network ocean closure for improved mean state and variability
by: Perezhogin, Pavel, et al.
Published: (2026)

Efficient n-body simulations using physics informed graph neural networks
by: Ramos-Osuna, Víctor, et al.
Published: (2025)

GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules
by: Ghorbani, Mahdi, et al.
Published: (2022)

Generalizable, real-time neural decoding with hybrid state-space models
by: Ryoo, Avery Hee-Woon, et al.
Published: (2025)

Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition
by: Mahmoud, Chiheb Ben, et al.
Published: (2024)

Repulsive Latent Score Distillation for Solving Inverse Problems
by: Zilberstein, Nicolas, et al.
Published: (2024)

Graph neural network for colliding particles with an application to sea ice floe modeling
by: Zhu, Ruibiao
Published: (2026)

Seeding neural network quantum states with tensor network states
by: Kaneko, Ryui, et al.
Published: (2025)

Towards a theory of learning dynamics in deep state space models
by: Smékal, Jakub, et al.
Published: (2024)

Distributed physics informed neural network for data-efficient solution to partial differential equations
by: Dwivedi, Vikas, et al.
Published: (2019)

GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
by: Huang, Xinquan, et al.
Published: (2023)

Scalable Implicit Graphon Learning
by: Azizpour, Ali, et al.
Published: (2024)

ML-SPEAK: A Theory-Guided Machine Learning Method for Studying and Predicting Conversational Turn-taking Patterns
by: O'Bryan, Lisa R., et al.
Published: (2024)

Investigating the generative dynamics of energy-based neural networks
by: Tausani, Lorenzo, et al.
Published: (2023)

Hyperbolic recurrent neural network as the first type of non-Euclidean neural quantum state ansatz
by: Dao, H. L.
Published: (2025)

Model-Driven Graph Contrastive Learning
by: Azizpour, Ali, et al.
Published: (2025)

Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems
by: Haitsiukevich, Katsiaryna, et al.
Published: (2024)

Fermionic neural Gibbs states
by: Nys, Jannes, et al.
Published: (2025)

Quantum neural networks facilitating quantum state classification
by: Sharma, Diksha, et al.
Published: (2025)

Adjoint sharding for very long context training of state space models
by: Xu, Xingzi, et al.
Published: (2025)

Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models
by: Tsampourakis, Kostas, et al.
Published: (2026)

Particle-MALA and Particle-mGRAD: Gradient-based MCMC methods for high-dimensional state-space models
by: Corenflos, Adrien, et al.
Published: (2024)

Variational quantization for state space models
by: David, Etienne, et al.
Published: (2024)

Learning Normalized Energy Models for Linear Inverse Problems
by: Zilberstein, Nicolas, et al.
Published: (2026)

Estimation of partially known Gaussian graphical models with score-based structural priors
by: Sevilla, Martín, et al.
Published: (2024)

Efficient physics-informed neural networks using hash encoding
by: Huang, Xinquan, et al.
Published: (2023)

Do graph neural network states contain graph properties?
by: Pelletreau-Duris, Tom, et al.
Published: (2024)

Observability conditions for neural state-space models with eigenvalues and their roots of unity
by: Gracyk, Andrew
Published: (2025)

Learning Time-Varying Turn-Taking Behavior in Group Conversations
by: Navarro, Madeline, et al.
Published: (2025)

Energy-based generator matching: A neural sampler for general state space
by: Woo, Dongyeop, et al.
Published: (2025)

Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
by: Shekarpaz, Simin, et al.
Published: (2023)