14 citations · 20 across the 9 of their papers we have counts for
9 papers
Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction
Mayanka Chandrashekar, Xi Zhang, Ethan Seefried +3
Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for foundation model inference. At sc…
Tabular Foundation Models for Multi-View Information Cascade Popularity Prediction
Wenting Zhu, Chenghua Gong, Sanchuan Guo +3
Predicting the future popularity of information cascades is essential for understanding information diffusion on social media. Despite recent advances, existing methods face two ke…
T3MAL: Test-Time Fast Adaptation for Robust Multi-Scale Information Diffusion Prediction
Wenting Zhu, Chaozhuo Li, Qingpo Yang +2
Information diffusion prediction (IDP) is a pivotal task for understanding how information propagates among users. Most existing methods commonly adhere to a conventional training-…
Navigating the Black Box: Leveraging LLMs for Effective Text-Level Graph Injection Attacks
Yuefei Lyu, Chaozhuo Li, Xi Zhang +1
Text-attributed graphs (TAGs) integrate textual data with graph structures, providing valuable insights in applications such as social network analysis and recommendation systems.…
Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification
Liwen Zheng, Chaozhuo Li, Zheng Liu +4
Fact verification plays a vital role in combating misinformation by assessing the veracity of claims through evidence retrieval and reasoning. However, traditional methods struggle…
Reasoning Paths as Signals: Augmenting Multi-hop Fact Verification through Structural Reasoning Progression
Liwen Zheng, Chaozhuo Li, Haoran Jia +1
The growing complexity of factual claims in real-world scenarios presents significant challenges for automated fact verification systems, particularly in accurately aggregating and…
Diffusion with a Linguistic Compass: Steering the Generation of Clinically Plausible Future sMRI Representations for Early MCI Conversion Prediction
Zhihao Tang, Chaozhuo Li, Litian Zhang +1
Early prediction of Mild Cognitive Impairment (MCI) conversion is hampered by a trade-off between immediacy--making fast predictions from a single baseline sMRI--and accuracy--leve…
GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
Peiyan Zhang, Yuchen Yan, Xi Zhang +5
In the realm of personalized recommender systems, the challenge of adapting to evolving user preferences and the continuous influx of new users and items is paramount. Conventional…
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Peiyan Zhang, Yuchen Yan, Xi Zhang +4
Graph Neural Networks (GNNs) have emerged as promising solutions for collaborative filtering (CF) through the modeling of user-item interaction graphs. The nucleus of existing GNN-…