6 papers · 1 filter
Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging
Chenrui Wu, Zexi Li, Jiajun Bu +2
Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful post-training paradigm that surpasses Supervised Fine-Tuning (SFT) in eliciting reasoning intelligenc…
Graph Neural Architecture Search with GPT-4
Haishuai Wang, Yang Gao, Xin Zheng +3
Graph Neural Architecture Search (GNAS) has shown promising results in finding the best graph neural network architecture on a given graph dataset. However, existing GNAS methods s…
Multi-Omics Analysis for Cancer Subtype Inference via Unrolling Graph Smoothness Priors
Jielong Lu, Zhihao Wu, Jiajun Yu +2
Integrating multi-omics datasets through data-driven analysis offers a comprehensive understanding of the complex biological processes underlying various diseases, particularly can…
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Yuanchen Bei, Sheng Zhou, Jinke Shi +3
Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real…
ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data
Mengxuan Li, Ke Liu, Jialong Guo +3
Healthcare data frequently contain a substantial proportion of missing values, necessitating effective time series imputation to support downstream disease diagnosis tasks. However…
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
Mengxuan Li, Ke Liu, Hongyang Chen +3
Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this t…