Copertina

Symbolic Graph Networks for Robust PDE Discovery from Noisy Sparse Data

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Xingyu, An, Junxiu, Guo, Jun, Zhou, Yuqian
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Artificial Intelligence I.2
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

FedNoisy: Federated Noisy Label Learning Benchmark
di: Liang, Siqi, et al.
Pubblicazione: (2023)

Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels
di: Wang, Fali, et al.
Pubblicazione: (2024)

Evaluating Model Robustness Using Adaptive Sparse L0 Regularization
di: Liu, Weiyou, et al.
Pubblicazione: (2024)

Artificial Scientific Discovery
di: Norelli, Antonio
Pubblicazione: (2024)

Quantifying First-Order Markov Violations in Noisy Reinforcement Learning: A Causal Discovery Approach
di: Mysore, Naveen
Pubblicazione: (2025)

Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis
di: Dong, Chang, et al.
Pubblicazione: (2024)

Finding Belief Geometries with Sparse Autoencoders
di: Levinson, Matthew
Pubblicazione: (2026)

CoATA: Effective Co-Augmentation of Topology and Attribute for Graph Neural Networks
di: Liu, Tao, et al.
Pubblicazione: (2025)

Differentiable Symbolic Planning: A Neural Architecture for Constraint Reasoning with Learned Feasibility
di: Oruganti, Venkatakrishna Reddy
Pubblicazione: (2026)

From Numbers to Prompts: A Cognitive Symbolic Transition Mechanism for Lightweight Time-Series Forecasting
di: Yoon, Namkyung, et al.
Pubblicazione: (2026)

Tempo: Compiled Dynamic Deep Learning with Symbolic Dependence Graphs
di: Silvestre, Pedro F., et al.
Pubblicazione: (2025)

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
di: Zhang, Jiaxing, et al.
Pubblicazione: (2023)

Shattered Compositionality: Counterintuitive Learning Dynamics of Transformers for Arithmetic
di: Zhao, Xingyu, et al.
Pubblicazione: (2026)

Generative and Contrastive Graph Representation Learning
di: Chen, Jiali, et al.
Pubblicazione: (2025)

Symbolic Neural Generation with Applications to Lead Discovery in Drug Design
di: Srinivasan, Ashwin, et al.
Pubblicazione: (2025)

Task-Conditioned Routing Signatures in Sparse Mixture-of-Experts Transformers
di: Avinash, Mynampati Sri Ranganadha
Pubblicazione: (2026)

CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning
di: Sauter, Andreas W. M., et al.
Pubblicazione: (2024)

Data Valuation by Fusing Global and Local Statistical Information
di: Zhou, Xiaoling, et al.
Pubblicazione: (2024)

Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
di: Kang, Jiyeon, et al.
Pubblicazione: (2025)

Graph-Eq: Discovering Mathematical Equations using Graph Generative Models
di: Ranasinghe, Nisal, et al.
Pubblicazione: (2025)

MetaSAEs: Joint Training with a Decomposability Penalty Produces More Atomic Sparse Autoencoder Latents
di: Levinson, Matthew
Pubblicazione: (2026)

Introducing New Node Prediction in Graph Mining: Predicting All Links from Isolated Nodes with Graph Neural Networks
di: Zanardini, Damiano, et al.
Pubblicazione: (2024)

Automating the Deep Space Network Data Systems; A Case Study in Adaptive Anomaly Detection through Agentic AI
di: Chou, Evan J., et al.
Pubblicazione: (2025)

QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives
di: Zhang, Xuzhi, et al.
Pubblicazione: (2025)

Beyond Prompt Engineering: Neuro-Symbolic-Causal Architecture for Robust Multi-Objective AI Agents
di: Akarlar, Gokturk Aytug
Pubblicazione: (2025)

Leveraging Personalized PageRank and Higher-Order Topological Structures for Heterophily Mitigation in Graph Neural Networks
di: Wang, Yumeng, et al.
Pubblicazione: (2025)

Multi-Scale Graph Learning for Anti-Sparse Downscaling
di: Fan, Yingda, et al.
Pubblicazione: (2025)

Fuzzy Convolution Neural Networks for Tabular Data Classification
di: Kulkarni, Arun D.
Pubblicazione: (2024)

Negative as Positive: Enhancing Out-of-distribution Generalization for Graph Contrastive Learning
di: Wang, Zixu, et al.
Pubblicazione: (2024)

DCV-ROOD Evaluation Framework: Dual Cross-Validation for Robust Out-of-Distribution Detection
di: Urrea-Castaño, Arantxa, et al.
Pubblicazione: (2025)

DELTA: Variational Disentangled Learning for Privacy-Preserving Data Reprogramming
di: Malarkkan, Arun Vignesh, et al.
Pubblicazione: (2025)

Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework
di: Gorpinich, M., et al.
Pubblicazione: (2025)

SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks
di: Vincze, Mátyás, et al.
Pubblicazione: (2024)

Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection
di: Kim, Heehyeon, et al.
Pubblicazione: (2023)

Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
di: Guo, Xu, et al.
Pubblicazione: (2024)

HEHRGNN: A Unified Embedding Model for Knowledge Graphs with Hyperedges and Hyper-Relational Edges
di: Rajagopalamenon, Rajesh, et al.
Pubblicazione: (2026)

Graph Neural Networks are Heuristics
di: Min, Yimeng, et al.
Pubblicazione: (2026)

FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data
di: Xiao, Zikai, et al.
Pubblicazione: (2024)

Node-Level Uncertainty Estimation in LLM-Generated SQL
di: Hasson, Hilaf, et al.
Pubblicazione: (2025)

DSF-GAN: DownStream Feedback Generative Adversarial Network
di: Perets, Oriel, et al.
Pubblicazione: (2024)