7 citations · 39 across the 33 of their papers we have counts for
8 papers · 1 filter
POND: Multi-Source Time Series Domain Adaptation with Information-Aware Prompt Tuning
Junxiang Wang, Guangji Bai, Wei Cheng +3
Time series domain adaptation stands as a pivotal and intricate challenge with diverse applications, including but not limited to human activity recognition, sleep stage classifica…
Open-ended Commonsense Reasoning with Unrestricted Answer Scope
Chen Ling, Xuchao Zhang, Xujiang Zhao +7
Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional…
DyExplainer: Explainable Dynamic Graph Neural Networks
Tianchun Wang, Dongsheng Luo, Wei Cheng +2
Graph Neural Networks (GNNs) resurge as a trending research subject owing to their impressive ability to capture representations from graph-structured data. However, the black-box…
A Survey on Detection of LLMs-Generated Content
Xianjun Yang, Liangming Pan, Xuandong Zhao +4
The burgeoning capabilities of advanced large language models (LLMs) such as ChatGPT have led to an increase in synthetic content generation with implications across a variety of s…
Zero-Shot Detection of Machine-Generated Codes
Xianjun Yang, Kexun Zhang, Haifeng Chen +3
This work proposes a training-free approach for the detection of LLMs-generated codes, mitigating the risks associated with their indiscriminate usage. To the best of our knowledge…
PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners
Yijia Xiao, Yiqiao Jin, Yushi Bai +10
The proliferation of Large Language Models (LLMs) has driven considerable interest in fine-tuning them with domain-specific data to create specialized language models. Nevertheless…
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
Xu Zheng, Farhad Shirani, Tianchun Wang +5
Graph Neural Networks (GNNs) are neural models that leverage the dependency structure in graphical data via message passing among the graph nodes. GNNs have emerged as pivotal arch…
Interpretable Imitation Learning with Dynamic Causal Relations
Tianxiang Zhao, Wenchao Yu, Suhang Wang +6
Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving…