Cover Image

Tensor-on-tensor Regression Neural Networks for Process Modeling with High-dimensional Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Qian, Bisheh, Mohammad N., Paynabar, Kamran
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Tensorized Multi-Task Learning for Personalized Modeling of Heterogeneous Individuals with High-Dimensional Data
by: Konyar, Elif, et al.
Published: (2025)

Stream-Based Active Learning for Process Monitoring
by: Capezza, Christian, et al.
Published: (2024)

Constrained Extreme Gradient Boosting for Adapting Reduced-Order Models
by: Baghi, Melika, et al.
Published: (2026)

An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity
by: Pyeon, Junghee, et al.
Published: (2025)

Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Properties
by: Patalano, Mariafrancesca, et al.
Published: (2025)

RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation
by: Moradi, Mehrdad, et al.
Published: (2025)

Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
by: Patalano, Mariafrancesca, et al.
Published: (2026)

Multivariate Time Series Data Imputation via Distributionally Robust Regularization
by: Liao, Che-Yi, et al.
Published: (2026)

High-Order Tensor Regression in Sparse Convolutional Neural Networks
by: Algarte, Roberto Dias
Published: (2025)

Bayesian Optimization for Function-Valued Responses under Min-Max Criteria
by: Ahadi, Pouya, et al.
Published: (2025)

Tensor-Based Foundations of Ordinary Least Squares and Neural Network Regression Models
by: Algarte, Roberto Dias
Published: (2024)

Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compression
by: Shi, Haoming, et al.
Published: (2026)

An Efficient Approach to Regression Problems with Tensor Neural Networks
by: Li, Yongxin, et al.
Published: (2024)

Generalized Factor Neural Network Model for High-dimensional Regression
by: Guo, Zichuan, et al.
Published: (2025)

Optimal Labeler Assignment and Sampling for Active Learning in the Presence of Imperfect Labels
by: Ahadi, Pouya, et al.
Published: (2025)

Neural Networks for Censored Expectile Regression Based on Data Augmentation
by: Cao, Wei, et al.
Published: (2025)

Semi-supervised Regression Analysis with Model Misspecification and High-dimensional Data
by: Tian, Ye, et al.
Published: (2024)

Robust Tensor-on-Tensor Regression
by: Hirari, Mehdi, et al.
Published: (2026)

Tucker Diffusion Model for High-dimensional Tensor Generation
by: Guo, Jianhua, et al.
Published: (2026)

Deep Neural Network Emulation of the Quantum-Classical Transition via Learned Wigner Function Dynamics
by: Majid, Kamran
Published: (2025)

Gaussian Process Regression -- Neural Network Hybrid with Optimized Redundant Coordinates
by: Manzhos, Sergei, et al.
Published: (2025)

Factor Augmented Tensor-on-Tensor Neural Networks
by: Zhou, Guanhao, et al.
Published: (2024)

How Does the ReLU Activation Affect the Implicit Bias of Gradient Descent on High-dimensional Neural Network Regression?
by: Lai, Kuo-Wei, et al.
Published: (2026)

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis
by: Podina, Lena, et al.
Published: (2024)

A Single Image Is All You Need: Zero-Shot Anomaly Localization Without Training Data
by: Moradi, Mehrdad, et al.
Published: (2025)

Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
by: Ashtiani, Shaghayegh Z., et al.
Published: (2024)

Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
by: Wang, Maolin, et al.
Published: (2023)

CP Degeneracy in Tensor Regression
by: Zhou, Ya, et al.
Published: (2020)

Robust Variable Selection for High-dimensional Regression with Missing Data and Measurement Errors
by: Zhang, Zhenhao, et al.
Published: (2024)

Tensor Product Neural Networks for Functional ANOVA Model
by: Park, Seokhun, et al.
Published: (2025)

Approximation Bounds for Recurrent Neural Networks with Application to Regression
by: Jiao, Yuling, et al.
Published: (2024)

Calibration Prediction Interval for Non-parametric Regression and Neural Networks
by: Wu, Kejin, et al.
Published: (2025)

Transformer Neural Processes - Kernel Regression
by: Jenson, Daniel, et al.
Published: (2024)

Online Forgetting Process for Linear Regression Models
by: Li, Yuantong, et al.
Published: (2020)

Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression
by: Lei, Mengying, et al.
Published: (2021)

Bayesian Physics-Informed Neural Networks for Inverse Problems (BPINN-IP): Application in Infrared Image Processing
by: Mohammad-Djafari, Ali, et al.
Published: (2025)

Conditional Uncertainty Quantification for Tensorized Topological Neural Networks
by: Wu, Yujia, et al.
Published: (2024)

Functional Tensor Decompositions for Physics-Informed Neural Networks
by: Vemuri, Sai Karthikeya, et al.
Published: (2024)

Probabilistic Pretraining for Neural Regression
by: Oreshkin, Boris N., et al.
Published: (2025)

Certified Continual Learning for Neural Network Regression
by: Pham, Long H., et al.
Published: (2024)