14 citations · 42 across the 26 of their papers we have counts for
8 papers · 1 filter
Temperature as a Meta-Policy: Adaptive Temperature in LLM Reinforcement Learning
Haoran Dang, Cuiling Lan, Hai Wan +2
Temperature is a crucial hyperparameter in large language models (LLMs), controlling the trade-off between exploration and exploitation during text generation. High temperatures en…
Advanced Global Wildfire Activity Modeling with Hierarchical Graph ODE
Fan Xu, Wei Gong, Hao Wu +6
Wildfires, as an integral component of the Earth system, are governed by a complex interplay of atmospheric, oceanic, and terrestrial processes spanning a vast range of spatiotempo…
Unlocking Out-of-Distribution Generalization in Dynamics through Physics-Guided Augmentation
Fan Xu, Hao Wu, Kun Wang +5
In dynamical system modeling, traditional numerical methods are limited by high computational costs, while modern data-driven approaches struggle with data scarcity and distributio…
GLADformer: A Mixed Perspective for Graph-level Anomaly Detection
Fan Xu, Nan Wang, Hao Wu +7
Graph-Level Anomaly Detection (GLAD) aims to distinguish anomalous graphs within a graph dataset. However, current methods are constrained by their receptive fields, struggling to…
Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum
Fan Xu, Nan Wang, Hao Wu +3
Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. In recent years, Graph Neural Networks (GNN) have been widely ap…
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
Fan Xu, Nan Wang, Xuezhi Wen +3
Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. It has gained substantial attention in…
Exploring Global and Local Information for Anomaly Detection with Normal Samples
Fan Xu, Nan Wang, Xibin Zhao
Anomaly detection aims to detect data that do not conform to regular patterns, and such data is also called outliers. The anomalies to be detected are often tiny in proportion, con…
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Xinwei Zhang, Jianwen Jiang, Yutong Feng +6
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new c…