22 citations · 83 across the 41 of their papers we have counts for
13 papers · 1 filter
Pretraining Reusable Inference Across Views with Synthetic Task Priors
Jielong Lu, Zhihao Wu, Jiajun Yu +2
Modern pretrained encoders make representations from heterogeneous views increasingly reusable, but the procedure that determines view utility and combines evidence is still relear…
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…
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…
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…
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search
Yang Gao, Hong Yang, Yizhi Chen +3
Graph Neural Architecture Search (GNAS) facilitates the automatic design of Graph Neural Networks (GNNs) tailored to specific downstream graph learning tasks. However, existing GNA…
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…
Repurposing Foundation Model for Generalizable Medical Time Series Classification
Nan Huang, Haishuai Wang, Zihuai He +2
Medical time series (MedTS) classification suffers from poor generalizability in real-world deployment due to inter- and intra-dataset heterogeneity, such as varying numbers of cha…
Towards a Unified Framework of Clustering-based Anomaly Detection
Zeyu Fang, Ming Gu, Sheng Zhou +4
Unsupervised Anomaly Detection (UAD) plays a crucial role in identifying abnormal patterns within data without labeled examples, holding significant practical implications across v…
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…
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values
Yu Zheng, Huan Yee Koh, Ming Jin +6
The detection of anomalies in multivariate time series data is crucial for various practical applications, including smart power grids, traffic flow forecasting, and industrial pro…
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
Yihe Wang, Yu Han, Haishuai Wang +1
Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. Howev…
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…
Temporal Feature Selection on Networked Time Series
Haishuai Wang, Jia Wu, Peng Zhang +1
This paper formulates the problem of learning discriminative features (\textit{i.e.,} segments) from networked time series data considering the linked information among time series…