graph neural network
Graph neural networks (GNNs) are a type of neural network designed to perform inference on graph-structured data, capturing relationships and dependencies among nodes effectively.
- $\texttt{STRCMP}$: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization
- Beyond Node-Centric Modeling: Sketching Signed Networks with Simplicial Complexes
- Bridging the Gap Between Cross-Domain Theory and Practical Application: A Case Study on Molecular Dissolution
- DiffLiG: Diffusion-enhanced Liquid Graph with Attention Propagation for Grid-to-Station Precipitation Correction
- Enforcing convex constraints in Graph Neural Networks
- FastJAM: a Fast Joint Alignment Model for Images
- GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
- Graph Neural Network Based Action Ranking for Planning
- Image Token Matters: Mitigating Hallucination in Discrete Tokenizer-based Large Vision-Language Models via Latent Editing
- Joint Relational Database Generation via Graph-Conditional Diffusion Models
- Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
- Personalized Image Editing in Text-to-Image Diffusion Models via Collaborative Direct Preference Optimization
- RIGNO: A Graph-based Framework For Robust And Accurate Operator Learning For PDEs On Arbitrary Domains
- S'MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning
- SegGraph: Leveraging Graphs of SAM Segments for Few-Shot 3D Part Segmentation
- Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks