45 citations · 134 across the 47 of their papers we have counts for
7 papers · 1 filter
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
Zifeng Ding, Heling Cai, Jingpei Wu +4
Modeling evolving knowledge over temporal knowledge graphs (TKGs) has become a heated topic. Various methods have been proposed to forecast links on TKGs. Most of them are embeddin…
GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models
Yuanchun Shen, Ruotong Liao, Zhen Han +2
While multi-modal models have successfully integrated information from image, video, and audio modalities, integrating graph modality into large language models (LLMs) remains unex…
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models
Ruotong Liao, Xu Jia, Yangzhe Li +2
The rapid advancements in large language models (LLMs) have ignited interest in the temporal knowledge graph (tKG) domain, where conventional embedding-based and rule-based methods…
Differentiable Quantum Architecture Search for Quantum Reinforcement Learning
Yize Sun, Yunpu Ma, Volker Tresp
Differentiable quantum architecture search (DQAS) is a gradient-based framework to design quantum circuits automatically in the NISQ era. It was motivated by such as low fidelity o…
Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models
Shuo Chen, Jindong Gu, Zhen Han +3
Various adaptation methods, such as LoRA, prompts, and adapters, have been proposed to enhance the performance of pre-trained vision-language models in specific domains. The robust…
QNEAT: Natural Evolution of Variational Quantum Circuit Architecture
Alessandro Giovagnoli, Yunpu Ma, Volker Tresp
Quantum Machine Learning (QML) is a recent and rapidly evolving field where the theoretical framework and logic of quantum mechanics are employed to solve machine learning tasks. V…
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Zifeng Ding, Jingpei Wu, Zongyue Li +2
Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knwoledge graph (TKG). Most previous TKGC methods only consider predic…