Copertina

Towards Multiscale Graph-based Protein Learning with Geometric Secondary Structural Motifs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Shih-Hsin, Huang, Yuhao, Transue, Taos, Baker, Justin, Forstater, Jonathan, Strohmer, Thomas, Wang, Bao
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Artificial Intelligence Numerical Analysis 68T07
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Improving Flow Matching by Aligning Flow Divergence
di: Huang, Yuhao, et al.
Pubblicazione: (2026)

Learning to Control the Smoothness of Graph Convolutional Network Features
di: Wang, Shih-Hsin, et al.
Pubblicazione: (2024)

Learning Decentralized Swarms Using Rotation Equivariant Graph Neural Networks
di: Transue, Taos, et al.
Pubblicazione: (2025)

Marrying Compressed Sensing and Deep Signal Separation
di: Hickok, Truman, et al.
Pubblicazione: (2024)

Meta-Auto-Decoder: A Meta-Learning Based Reduced Order Model for Solving Parametric Partial Differential Equations
di: Ye, Zhanhong, et al.
Pubblicazione: (2023)

Learned RESESOP for solving inverse problems with inexact forward operator
di: Feinler, Mathias S., et al.
Pubblicazione: (2024)

Accuracy and stability of Artificial Neural Networks for HP-Splines frequency parameter selection
di: Bruni, Vittoria, et al.
Pubblicazione: (2026)

Multiscale Neural Networks for Approximating Green's Functions
di: Hao, Wenrui, et al.
Pubblicazione: (2024)

RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation
di: Huang, Yuhao, et al.
Pubblicazione: (2026)

Geometric Generalization of Neural Operators from Kernel Integral Perspective
di: Han, Mingyu, et al.
Pubblicazione: (2026)

GAN-based iterative motion estimation in HASTE MRI
di: Feinler, Mathias S., et al.
Pubblicazione: (2024)

Anderson Accelerated Gauss-Newton-guided deep learning for nonlinear inverse problems with Application to Electrical Impedance Tomography
di: Zhou, Qingping, et al.
Pubblicazione: (2023)

Single-shot prediction of parametric partial differential equations
di: Rafiq, Khalid, et al.
Pubblicazione: (2025)

Embedding Inequalities for Barron-type Spaces
di: Wu, Lei
Pubblicazione: (2023)

Approximation theory for 1-Lipschitz ResNets
di: Murari, Davide, et al.
Pubblicazione: (2025)

Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noise
di: Shen, Haoran, et al.
Pubblicazione: (2024)

Sparse Implementation of Versatile Graph-Informed Layers
di: Della Santa, Francesco
Pubblicazione: (2024)

From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNs
di: Yamchote, Phaphontee, et al.
Pubblicazione: (2025)

An $r$-adaptive finite element method using neural networks for parametric self-adjoint elliptic problem
di: Aballay, Danilo, et al.
Pubblicazione: (2025)

Neural Ordinary Differential Equations for Model Order Reduction of Stiff Systems
di: Caldana, Matteo, et al.
Pubblicazione: (2024)

Improvements on uncertainty quantification with variational autoencoders
di: Tonini, Andrea, et al.
Pubblicazione: (2025)

Deep collocation method: A framework for solving PDEs using neural networks with error control
di: Weng, Mingxing, et al.
Pubblicazione: (2025)

ECALL: Expectation-calibrated learning for unsupervised blind deconvolution
di: Haltmeier, Markus, et al.
Pubblicazione: (2024)

A Least-Squares-Based Regularity-Conforming Neural Networks (LS-ReCoNNs) for Solving Parametric Transmission Problems
di: Baharlouei, Shima, et al.
Pubblicazione: (2026)

Recurrent Neural Operators: Stable Long-Term PDE Prediction
di: Ye, Zaijun, et al.
Pubblicazione: (2025)

Quantitative Approximation Rates for Group Equivariant Learning
di: Siegel, Jonathan W., et al.
Pubblicazione: (2026)

Energy Dissipation Rate Guided Adaptive Sampling for Physics-Informed Neural Networks: Resolving Surface-Bulk Dynamics in Allen-Cahn Systems
di: Li, Chunyan, et al.
Pubblicazione: (2025)

Conservative approximation-based feedforward neural network for WENO schemes
di: Park, Kwanghyuk, et al.
Pubblicazione: (2025)

Expansive Natural Neural Gradient Flows for Energy Minimization
di: Dahmen, Wolfgang, et al.
Pubblicazione: (2025)

A Generalization Bound for a Family of Implicit Networks
di: Fung, Samy Wu, et al.
Pubblicazione: (2024)

Tensor Neural Network Based Machine Learning Method for Elliptic Multiscale Problems
di: Lin, Zhongshuo, et al.
Pubblicazione: (2024)

Reduced Order Modeling of Partial Differential Equations on Parameter-Dependent Domains Using Deep Neural Networks
di: Bukač, Martina, et al.
Pubblicazione: (2024)

Learning Geometric-Aware Quadrature Rules for Functional Minimization
di: Smaragdakis, Costas
Pubblicazione: (2025)

An algorithmic approach to direct spline products: procedures and computational aspects
di: Patrizi, Francesco, et al.
Pubblicazione: (2026)

Scientific machine learning for closure models in multiscale problems: a review
di: Sanderse, Benjamin, et al.
Pubblicazione: (2024)

Neural enrichment finite element method: A hybrid framework for problems with strong oscillations or interface problems
di: Guo, Shihan, et al.
Pubblicazione: (2026)

Discontinuous hybrid neural networks for the one-dimensional partial differential equations
di: Wang, Xiaoyu, et al.
Pubblicazione: (2025)

Subspace method based on neural networks for eigenvalue problems
di: Dai, Xiaoying, et al.
Pubblicazione: (2024)

Towards Model Discovery Using Domain Decomposition and PINNs
di: Saha, Tirtho S., et al.
Pubblicazione: (2024)

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
di: Codega, Gabriele, et al.
Pubblicazione: (2025)