Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Liu, Hao, Dahal, Biraj, Lai, Rongjie, Liao, Wenjing |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Data-Driven Model Reduction using WeldNet: Windowed Encoders for Learning Dynamics
by: Dahal, Biraj, et al.
Published: (2025)
by: Dahal, Biraj, et al.
Published: (2025)
Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer
by: Hsu, Alexander, et al.
Published: (2026)
by: Hsu, Alexander, et al.
Published: (2026)
Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees
by: Zhang, Zecheng, et al.
Published: (2025)
by: Zhang, Zecheng, et al.
Published: (2025)
Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
by: Shen, Zhaiming, et al.
Published: (2025)
by: Shen, Zhaiming, et al.
Published: (2025)
Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
by: Shen, Zhaiming, et al.
Published: (2025)
by: Shen, Zhaiming, et al.
Published: (2025)
Unsupervised Solution Operator Learning for Mean-Field Games via Sampling-Invariant Parametrizations
by: Huang, Han, et al.
Published: (2024)
by: Huang, Han, et al.
Published: (2024)
Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds
by: Li, Xiao, et al.
Published: (2024)
by: Li, Xiao, et al.
Published: (2024)
Towards Establishing Guaranteed Error for Learned Database Operations
by: Zeighami, Sepanta, et al.
Published: (2024)
by: Zeighami, Sepanta, et al.
Published: (2024)
Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
by: Feng, Xue, et al.
Published: (2026)
by: Feng, Xue, et al.
Published: (2026)
Interpretable Syntactic Representations Enable Hierarchical Word Vectors
by: Silwal, Biraj
Published: (2024)
by: Silwal, Biraj
Published: (2024)
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study
by: Liu, Hao, et al.
Published: (2024)
by: Liu, Hao, et al.
Published: (2024)
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification
by: Yang, Hanxuan, et al.
Published: (2024)
by: Yang, Hanxuan, et al.
Published: (2024)
Adaptive Graph Auto-Encoder for General Data Clustering
by: Li, Xuelong, et al.
Published: (2020)
by: Li, Xuelong, et al.
Published: (2020)
Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs
by: Taniguchi, Koichi, et al.
Published: (2026)
by: Taniguchi, Koichi, et al.
Published: (2026)
KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning
by: Yu, Fangchen, et al.
Published: (2024)
by: Yu, Fangchen, et al.
Published: (2024)
Diffusion Bridge AutoEncoders for Unsupervised Representation Learning
by: Kim, Yeongmin, et al.
Published: (2024)
by: Kim, Yeongmin, et al.
Published: (2024)
Differential Informed Auto-Encoder
by: Zhang, Jinrui
Published: (2024)
by: Zhang, Jinrui
Published: (2024)
Discrete Graph Auto-Encoder
by: Boget, Yoann, et al.
Published: (2023)
by: Boget, Yoann, et al.
Published: (2023)
Knowledge-integrated AutoEncoder Model
by: Lazebnik, Teddy, et al.
Published: (2023)
by: Lazebnik, Teddy, et al.
Published: (2023)
Learning Multimodal Energy-Based Model with Multimodal Variational Auto-Encoder via MCMC Revision
by: Cui, Jiali, et al.
Published: (2026)
by: Cui, Jiali, et al.
Published: (2026)
Norm Augmented Graph AutoEncoders for Link Prediction
by: Liu, Yunhui, et al.
Published: (2025)
by: Liu, Yunhui, et al.
Published: (2025)
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection
by: Srinivasan, Abhishek, et al.
Published: (2025)
by: Srinivasan, Abhishek, et al.
Published: (2025)
Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
by: Liu, Hao, et al.
Published: (2024)
by: Liu, Hao, et al.
Published: (2024)
Learning Network Representations with Disentangled Graph Auto-Encoder
by: Fan, Di, et al.
Published: (2024)
by: Fan, Di, et al.
Published: (2024)
Classification of Deceased Patients from Non-Deceased Patients using Random Forest and Support Vector Machine Classifiers
by: Saha, Dheeman, et al.
Published: (2024)
by: Saha, Dheeman, et al.
Published: (2024)
SOSAE: Self-Organizing Sparse AutoEncoder
by: Modi, Sarthak Ketanbhai, et al.
Published: (2025)
by: Modi, Sarthak Ketanbhai, et al.
Published: (2025)
AEMLO: AutoEncoder-Guided Multi-Label Oversampling
by: Zhou, Ao, et al.
Published: (2024)
by: Zhou, Ao, et al.
Published: (2024)
Golden Ratio-Based Sufficient Dimension Reduction
by: Yang, Wenjing, et al.
Published: (2024)
by: Yang, Wenjing, et al.
Published: (2024)
Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection
by: Dinh, Phai Vu, et al.
Published: (2024)
by: Dinh, Phai Vu, et al.
Published: (2024)
Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders
by: Wang, Shun, et al.
Published: (2025)
by: Wang, Shun, et al.
Published: (2025)
Representation Learning of Lab Values via Masked AutoEncoders
by: Restrepo, David, et al.
Published: (2025)
by: Restrepo, David, et al.
Published: (2025)
Basis-to-Basis Operator Learning Using Function Encoders
by: Ingebrand, Tyler, et al.
Published: (2024)
by: Ingebrand, Tyler, et al.
Published: (2024)
In-Context Operator Learning on the Space of Probability Measures
by: Cole, Frank, et al.
Published: (2026)
by: Cole, Frank, et al.
Published: (2026)
Disentangled and Distilled Encoder for Out-of-Distribution Reasoning with Rademacher Guarantees
by: Rahiminasab, Zahra, et al.
Published: (2025)
by: Rahiminasab, Zahra, et al.
Published: (2025)
Learning Theory of Transformers: Local-to-Global Approximation via Softmax Partition of Unity
by: Shi, Zhongjie, et al.
Published: (2026)
by: Shi, Zhongjie, et al.
Published: (2026)
Statistical Test for Anomaly Detections by Variational Auto-Encoders
by: Miwa, Daiki, et al.
Published: (2024)
by: Miwa, Daiki, et al.
Published: (2024)
Undermining Federated Learning Accuracy in EdgeIoT via Variational Graph Auto-Encoders
by: Li, Kai, et al.
Published: (2025)
by: Li, Kai, et al.
Published: (2025)
Physically Interpretable Representation Learning with Gaussian Mixture Variational AutoEncoder (GM-VAE)
by: Fan, Tiffany, et al.
Published: (2025)
by: Fan, Tiffany, et al.
Published: (2025)
On the Interpolation Error of Nonlinear Attention versus Linear Regression
by: Liao, Zhenyu, et al.
Published: (2025)
by: Liao, Zhenyu, et al.
Published: (2025)
Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning
by: Fan, Di, et al.
Published: (2023)
by: Fan, Di, et al.
Published: (2023)
Similar Items
-
Data-Driven Model Reduction using WeldNet: Windowed Encoders for Learning Dynamics
by: Dahal, Biraj, et al.
Published: (2025) -
Understanding In-Context Learning for Nonlinear Regression with Transformers: Attention as Featurizer
by: Hsu, Alexander, et al.
Published: (2026) -
Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees
by: Zhang, Zecheng, et al.
Published: (2025) -
Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods
by: Shen, Zhaiming, et al.
Published: (2025) -
Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights
by: Shen, Zhaiming, et al.
Published: (2025)