:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Liu, Gang, Chen, Jie, Zhu, Yihan, Sun, Michael, Luo, Tengfei, Chawla, Nitesh V, Jiang, Meng
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	https://arxiv.org/abs/2510.08744
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Learning Repetition-Invariant Representations for Polymer Informatics
por: Zhu, Yihan, et al.
Publicado: (2025)

Graph Diffusion Transformers for Multi-Conditional Molecular Generation
por: Liu, Gang, et al.
Publicado: (2024)

AgentDrug: Utilizing Large Language Models in An Agentic Workflow for Zero-Shot Molecular Editing
por: Le, Khiem, et al.
Publicado: (2024)

UGMAE: A Unified Framework for Graph Masked Autoencoders
por: Tian, Yijun, et al.
Publicado: (2024)

HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
por: Ma, Yihong, et al.
Publicado: (2023)

A Survey of Large Language Models for Graphs
por: Ren, Xubin, et al.
Publicado: (2024)

Fast Explanations via Policy Gradient-Optimized Explainer
por: Pan, Deng, et al.
Publicado: (2024)

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE
por: Le, Khiem, et al.
Publicado: (2025)

POINT$^{2}$: A Polymer Informatics Training and Testing Database
por: Xu, Jiaxin, et al.
Publicado: (2025)

GFT: Graph Foundation Model with Transferable Tree Vocabulary
por: Wang, Zehong, et al.
Publicado: (2024)

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
por: Wang, Zehong, et al.
Publicado: (2024)

Self-Supervised Learning on Molecular Graphs: A Systematic Investigation of Masking Design
por: Yang, Jiannan, et al.
Publicado: (2025)

AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive
por: Guo, Taicheng, et al.
Publicado: (2026)

Beyond Message Passing: Neural Graph Pattern Machine
por: Wang, Zehong, et al.
Publicado: (2025)

Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillation
por: Tian, Yijun, et al.
Publicado: (2024)

Graph Neural Prompting with Large Language Models
por: Tian, Yijun, et al.
Publicado: (2023)

Controllable Molecular Generative Foundation Models
por: Zhu, Yihan, et al.
Publicado: (2026)

Neighbourhood Transformer: Switchable Attention for Monophily-Aware Graph Learning
por: Luo, Yi, et al.
Publicado: (2026)

JTreeformer: Graph-Transformer via Latent-Diffusion Model for Molecular Generation
por: Shi, Ji, et al.
Publicado: (2025)

Supercharging Graph Transformers with Advective Diffusion
por: Wu, Qitian, et al.
Publicado: (2023)

Physics-Informed Teleconnection-Aware Transformer for Global Subseasonal-to-Seasonal Forecasting
por: Lyu, Tengfei, et al.
Publicado: (2025)

MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
por: Yan, Zimo, et al.
Publicado: (2025)

STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models
por: Chen, Liyue, et al.
Publicado: (2025)

MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs
por: Nguyen, Quang H., et al.
Publicado: (2024)

Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction
por: Ma, Tengfei, et al.
Publicado: (2023)

Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learning
por: Zhang, Yihan, et al.
Publicado: (2026)

Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis
por: Ma, Yijun, et al.
Publicado: (2026)

Molecular De Novo Design through Transformer-based Reinforcement Learning
por: Xu, Pengcheng, et al.
Publicado: (2023)

Universal Link Predictor By In-Context Learning on Graphs
por: Dong, Kaiwen, et al.
Publicado: (2024)

Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning
por: Wu, Lirong, et al.
Publicado: (2024)

Exploring Diffusion Transformer Designs via Grafting
por: Chandrasegaran, Keshigeyan, et al.
Publicado: (2025)

Exploring the Global-to-Local Attention Scheme in Graph Transformers: An Empirical Study
por: Wu, Gang, et al.
Publicado: (2025)

Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks
por: Tang, Ziyuan, et al.
Publicado: (2025)

Chain-of-Generation: Progressive Latent Diffusion for Text-Guided Molecular Design
por: Li, Lingxiao, et al.
Publicado: (2025)

One-Layer Transformer Provably Learns One-Nearest Neighbor In Context
por: Li, Zihao, et al.
Publicado: (2024)

Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep Research
por: Liu, Gang, et al.
Publicado: (2025)

Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction
por: Wu, Chenghan, et al.
Publicado: (2026)

Graph Foundation Models: A Comprehensive Survey
por: Wang, Zehong, et al.
Publicado: (2025)

Continuous Diffusion Transformers for Designing Synthetic Regulatory Elements
por: Liu, Jonathan, et al.
Publicado: (2026)

Are Large Language Models In-Context Graph Learners?
por: Li, Jintang, et al.
Publicado: (2025)