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48 papers · 1 filter
Learning to Discretize: Diffusion-Based Adaptive Mesh with Spectral Guidance
Zixuan Shen, Bingchuan Wang, Zhi Wang +1
Most neural partial differential equation (PDE) surrogates learn how fields evolve after a grid has already been chosen. However, before any operator is applied, the grid has alrea…
LABO: LLM-Accelerated Bayesian Optimization through Broad Exploration and Selective Experimentation
Zhuo Chen, Xinzhe Yuan, Jianshu Zhang +8
The high cost and data scarcity in scientific exploration have motivated the use of large language models (LLMs) as knowledge-driven components in Bayesian optimization (BO). Howev…
Adaptive NN graph model
Jiaye Li, Hang Xu, Shichao Zhang
The -nearest neighbors (NN) algorithm is a cornerstone of non-parametric classification in artificial intelligence, yet its deployment in large-scale applications is persiste…
Beyond Monotonicity: Revisiting Factorization Principles in Multi-Agent Q-Learning
Tianmeng Hu, Yongzheng Cui, Rui Tang +2
Value decomposition is a central approach in multi-agent reinforcement learning (MARL), enabling centralized training with decentralized execution by factorizing the global value f…
Contrastive Regularization over LoRA for Multimodal Biomedical Image Incremental Learning
Haojie Zhang, Yixiong Liang, Hulin Kuang +5
Multimodal Biomedical Image Incremental Learning (MBIIL) is essential for handling diverse tasks and modalities in the biomedical domain, as training separate models for each modal…
Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detection
Tairan Huang, Yili Wang, Qiutong Li +2
Graph fraud detection has garnered significant attention as Graph Neural Networks (GNNs) have proven effective in modeling complex relationships within multimodal data. However, ex…