:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Chang, Zenghui, Zhang, Yang, Tan, Hu, Chen, Hong Cai
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	https://arxiv.org/abs/2503.22313
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Neural Network Graph Similarity Computation Based on Graph Fusion
por: Chang, Zenghui, et al.
Publicado: (2025)

RNNs, CNNs and Transformers in Human Action Recognition: A Survey and a Hybrid Model
por: Alomar, Khaled, et al.
Publicado: (2024)

On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
por: Eliasof, Moshe, et al.
Publicado: (2024)

Modeling Neural Networks with Privacy Using Neural Stochastic Differential Equations
por: Hong, Sanghyun, et al.
Publicado: (2025)

Efficient Sparse Selective-Update RNNs for Long-Range Sequence Modeling
por: Yin, Bojian, et al.
Publicado: (2026)

Semi-Implicit Neural Ordinary Differential Equations
por: Zhang, Hong, et al.
Publicado: (2024)

Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics
por: Almanasreh, Marah, et al.
Publicado: (2026)

Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
por: Chen, Chuqi, et al.
Publicado: (2024)

A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains
por: Ling, Shuo, et al.
Publicado: (2024)

A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations
por: Yang, Qihong, et al.
Publicado: (2026)

Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation
por: Ge, Chendi, et al.
Publicado: (2025)

Modeling Time Series Dynamics with Fourier Ordinary Differential Equations
por: Guo, Muhao, et al.
Publicado: (2025)

Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
por: Oh, YongKyung, et al.
Publicado: (2025)

Universal Time Series Generation with Neural Controlled Differential Equations
por: Berndt, Torben, et al.
Publicado: (2026)

Learning to (Learn at Test Time): RNNs with Expressive Hidden States
por: Sun, Yu, et al.
Publicado: (2024)

Renaissance of RNNs in Streaming Clinical Time Series: Compact Recurrence Remains Competitive with Transformers
por: Tong, Ran, et al.
Publicado: (2025)

Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series
por: Rajaei, Pedram, et al.
Publicado: (2025)

On the Role of Depth in the Expressivity of RNNs
por: Lizaire, Maude, et al.
Publicado: (2026)

PAC-Bayes Generalisation Bounds for Dynamical Systems Including Stable RNNs
por: Eringis, Deividas, et al.
Publicado: (2023)

Neural Controlled Differential Equations with Quantum Hidden Evolutions
por: Yang, Lingyi, et al.
Publicado: (2024)

Mechanistic Interpretability of RNNs emulating Hidden Markov Models
por: Torre, Elia, et al.
Publicado: (2025)

GeoMaNO: Geometric Mamba Neural Operator for Partial Differential Equations
por: Han, Xi, et al.
Publicado: (2025)

A Universal Model Combining Differential Equations and Neural Networks for Ball Trajectory Prediction
por: Shi, Zhiwei, et al.
Publicado: (2025)

Neural Differential Appearance Equations
por: Liu, Chen, et al.
Publicado: (2024)

Symbolic Neural Ordinary Differential Equations
por: Li, Xin, et al.
Publicado: (2025)

Comprehensive Review of Neural Differential Equations for Time Series Analysis
por: Oh, YongKyung, et al.
Publicado: (2025)

M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling
por: Mishra, Mayank, et al.
Publicado: (2026)

Continuum Dropout for Neural Differential Equations
por: Lee, Jonghun, et al.
Publicado: (2025)

Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations
por: Qin, Tiexin, et al.
Publicado: (2023)

From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption
por: Bose, Shourya, et al.
Publicado: (2024)

Governing Equation Discovery from Data Based on Differential Invariants
por: Hu, Lexiang, et al.
Publicado: (2025)

Comba: Improving Bilinear RNNs with Closed-loop Control
por: Hu, Jiaxi, et al.
Publicado: (2025)

CGNSDE: Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation
por: Chen, Chuanqi, et al.
Publicado: (2024)

Implicit Language Models are RNNs: Balancing Parallelization and Expressivity
por: Schöne, Mark, et al.
Publicado: (2025)

Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation
por: Zhang, Bo, et al.
Publicado: (2024)

Detecting Invariant Manifolds in ReLU-Based RNNs
por: Eisenmann, Lukas, et al.
Publicado: (2025)

Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations
por: Meng, Bin, et al.
Publicado: (2024)

Solving Time-Fractional Partial Integro-Differential Equations Using Tensor Neural Network
por: Lin, Zhongshuo, et al.
Publicado: (2025)

Continuously Evolving Graph Neural Controlled Differential Equations for Traffic Forecasting
por: Wu, Jiajia, et al.
Publicado: (2024)

Memory Caching: RNNs with Growing Memory
por: Behrouz, Ali, et al.
Publicado: (2026)