Copertina

DNS: Data-driven Nonlinear Smoother for Complex Model-free Process

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Cumlin, Fredrik, Ghosh, Anubhab, Chatterjee, Saikat
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Signal Processing Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

VSE: Variational state estimation of complex model-free process
di: Norén, Gustav, et al.
Pubblicazione: (2026)

DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setup
di: Ghosh, Anubhab, et al.
Pubblicazione: (2023)

pDANSE: Particle-based Data-driven Nonlinear State Estimation from Nonlinear Measurements
di: Ghosh, Anubhab, et al.
Pubblicazione: (2025)

Semi-Supervised Model-Free Bayesian State Estimation from Compressed Measurements
di: Ghosh, Anubhab, et al.
Pubblicazione: (2024)

Leveraging LLMs for Scalable Non-intrusive Speech Quality Assessment
di: Cumlin, Fredrik, et al.
Pubblicazione: (2025)

Rho-Perfect: Correlation Ceiling For Subjective Evaluation Datasets
di: Cumlin, Fredrik
Pubblicazione: (2026)

AI-Aided Kalman Filters
di: Shlezinger, Nir, et al.
Pubblicazione: (2024)

Learning the Influence Graph of a High-Dimensional Markov Process with Memory
di: Bagewadi, Smita, et al.
Pubblicazione: (2024)

Data-driven Nonlinear Modal Analysis with Physics-constrained Deep Learning: Numerical and Experimental Study
di: Rostamijavanani, Abdolvahhab, et al.
Pubblicazione: (2025)

Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data
di: Hota, Aritra, et al.
Pubblicazione: (2024)

Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectives
di: Schneider, Linda-Sophie, et al.
Pubblicazione: (2024)

Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
di: Hippert-Ferrer, Alexandre, et al.
Pubblicazione: (2025)

SA-SSL-MOS: Self-supervised Learning MOS Prediction with Spectral Augmentation for Generalized Multi-Rate Speech Assessment
di: Cao, Fengyuan, et al.
Pubblicazione: (2026)

Uncertainty quantification in neural network classifiers -- a local linear approach
di: Malmström, Magnus, et al.
Pubblicazione: (2023)

Adaptive Attention-Based Model for 5G Radio-based Outdoor Localization
di: Yaman, Ilayda, et al.
Pubblicazione: (2025)

Bayesian Learning in a Nonlinear Multiscale State-Space Model
di: Vélez-Cruz, Nayely, et al.
Pubblicazione: (2024)

Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering
di: Zhao, Dafang, et al.
Pubblicazione: (2024)

Self-Trained Model for ECG Complex Delineation
di: Avetisyan, Aram, et al.
Pubblicazione: (2024)

On the Detection and Quantification of Nonlinearity via Statistics of the Gradients of a Black-Box Model
di: Tsialiamanis, G., et al.
Pubblicazione: (2023)

Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring
di: McKinney, Matthew, et al.
Pubblicazione: (2024)

Unmixing Noise from Hawkes Process to Model Learned Physiological Events
di: Staerman, Guillaume, et al.
Pubblicazione: (2024)

Neural Probabilistic Amplitude Shaping for Nonlinear Fiber Channels
di: Askari, Mohammad Taha, et al.
Pubblicazione: (2026)

Nonlinearity and Uncertainty Informed Moment-Matching Gaussian Mixture Splitting
di: Kulik, Jackson, et al.
Pubblicazione: (2024)

Kernel Operator-Theoretic Bayesian Filter for Nonlinear Dynamical Systems
di: Li, Kan, et al.
Pubblicazione: (2024)

A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding
di: Karkan, Ali Hasanzadeh, et al.
Pubblicazione: (2026)

Computational Complexity Evaluation of Neural Network Applications in Signal Processing
di: Freire, Pedro, et al.
Pubblicazione: (2022)

Neural Nonlinear Shrinkage of Covariance Matrices for Minimum Variance Portfolio Optimization
di: Yang, Liusha, et al.
Pubblicazione: (2026)

Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
di: Koronaki, Eleni D., et al.
Pubblicazione: (2024)

Sensor Selection via GFlowNets: A Deep Generative Modeling Framework to Navigate Combinatorial Complexity
di: Evmorfos, Spilios, et al.
Pubblicazione: (2024)

A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites
di: Karkan, Ali Hasanzadeh, et al.
Pubblicazione: (2025)

Phase-driven Domain Generalizable Learning for Nonstationary Time Series
di: Mohapatra, Payal, et al.
Pubblicazione: (2024)

Network scaling and scale-driven loss balancing for intelligent poroelastography
di: Xu, Yang, et al.
Pubblicazione: (2024)

Data-driven tool wear prediction in milling, based on a process-integrated single-sensor approach
di: Hirsch, Eric, et al.
Pubblicazione: (2024)

Classification of IED-free EEG Responses for Assisted Epilepsy Diagnosis
di: Zanardini, Giacomo, et al.
Pubblicazione: (2026)

HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers
di: Ghosh, Debamita, et al.
Pubblicazione: (2023)

Data Augmentation for Seizure Prediction with Generative Diffusion Model
di: Shu, Kai, et al.
Pubblicazione: (2023)

Regularized Linear Discriminant Analysis Using a Nonlinear Covariance Matrix Estimator
di: Mahadi, Maaz, et al.
Pubblicazione: (2024)

Topological Kalman Filtering on Cell Complexes
di: Liu, Chengen, et al.
Pubblicazione: (2026)

Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems
di: Lin, Zhidi, et al.
Pubblicazione: (2025)

[Re] The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Non-Gaussian Observation Models
di: Casco-Rodriguez, Josue, et al.
Pubblicazione: (2024)