Cover Image

Sample Efficient Learning of Factored Embeddings of Tensor Fields

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Heo, Taemin, Bajaj, Chandrajit
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Coseparable Nonnegative Tensor Factorization With T-CUR Decomposition
by: Chen, Juefei, et al.
Published: (2024)

Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion
by: Su, Bowen, et al.
Published: (2024)

Wedge Sampling: Efficient Tensor Completion with Nearly-Linear Sample Complexity
by: Luo, Hengrui, et al.
Published: (2026)

Tensor Completion with Nearly Linear Samples Given Weak Side Information
by: Yu, Christina Lee, et al.
Published: (2020)

Efficient Tensor Completion Algorithms for Highly Oscillatory Operators
by: Singh, Navjot, et al.
Published: (2025)

Tensor Decomposition Meets RKHS: Efficient Algorithms for Smooth and Misaligned Data
by: Larsen, Brett W., et al.
Published: (2024)

RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
by: Song, Zhenao
Published: (2025)

A New Tensor Network: Tubal Tensor Train and Its Applications
by: Ahmadi-Asl, Salman, et al.
Published: (2026)

Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
by: Chen, Yifan, et al.
Published: (2023)

Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints
by: Lopez-Piqueres, Javier, et al.
Published: (2024)

Sharp Analysis of Power Iteration for Tensor PCA
by: Wu, Yuchen, et al.
Published: (2024)

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling
by: Chatalic, Antoine, et al.
Published: (2023)

Generalized Canonical Polyadic Tensor Decompositions with General Symmetry
by: Mulrooney, Alex, et al.
Published: (2026)

Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields
by: Weiland, Tim, et al.
Published: (2025)

The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
by: Adcock, Ben, et al.
Published: (2024)

Time Extrapolation with Graph Convolutional Autoencoder and Tensor Train Decomposition
by: Chen, Yuanhong, et al.
Published: (2025)

Variational Bayesian Inference for Tensor Robust Principal Component Analysis
by: Wang, Chao, et al.
Published: (2024)

Quantile-Based Randomized Kaczmarz for Corrupted Tensor Linear Systems
by: Castillo, Alejandra, et al.
Published: (2025)

TensorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices
by: Xu, Mingxue, et al.
Published: (2025)

Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
by: Cai, HanQin, et al.
Published: (2022)

Lower Bounds for the Convergence of Tensor Power Iteration on Random Overcomplete Models
by: Wu, Yuchen, et al.
Published: (2022)

Convolutional Surrogate for 3D Discrete Fracture-Matrix Tensor Upscaling
by: Špetlík, Martin, et al.
Published: (2026)

Tensor train completion: local recovery guarantees via Riemannian optimization
by: Budzinskiy, Stanislav, et al.
Published: (2021)

Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction
by: Yuan, Longhao, et al.
Published: (2019)

On Spectral Learning for Odeco Tensors: Perturbation, Initialization, and Algorithms
by: Auddy, Arnab, et al.
Published: (2025)

Geometry is All You Need: A Unified Taxonomy of Matrix and Tensor Factorization for Compression of Generative Language Models
by: Xu, Mingxue, et al.
Published: (2024)

An Efficient Deep Learning Approach for Approximating Parameter-to-Solution Maps of PDEs
by: Lei, Guanhang, et al.
Published: (2024)

Sketching the Heat Kernel: Using Gaussian Processes to Embed Data
by: Gilbert, Anna C., et al.
Published: (2024)

Monotone Generative Modeling via a Gromov-Monge Embedding
by: Lee, Wonjun, et al.
Published: (2023)

An End-to-End Deep Learning Method for Solving Nonlocal Allen-Cahn and Cahn-Hilliard Phase-Field Models
by: Geng, Yuwei, et al.
Published: (2024)

Self-Balancing, Memory Efficient, Dynamic Metric Space Data Maintenance, for Rapid Multi-Kernel Estimation
by: Ellendula, Aditya S, et al.
Published: (2025)

Tensor Decomposition with Unaligned Observations
by: Tang, Runshi, et al.
Published: (2024)

Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent
by: Bian, Fengmiao, et al.
Published: (2025)

Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
by: Jalalian, Yasamin, et al.
Published: (2025)

On the Convergence of Irregular Sampling in Reproducing Kernel Hilbert Spaces
by: Iske, Armin
Published: (2025)

Decoupled Diffusion Sampling for Inverse Problems on Function Spaces
by: Lin, Thomas Y. L., et al.
Published: (2026)

Free-RBF-KAN: Kolmogorov-Arnold Networks with Adaptive Radial Basis Functions for Efficient Function Learning
by: Chiu, Shao-Ting, et al.
Published: (2026)

FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations
by: Ye, Zaijun, et al.
Published: (2024)

Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference
by: Chuang, Pi-Yueh, et al.
Published: (2025)

Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
by: Tang, Kejun, et al.
Published: (2023)