10 citations · 35 across the 11 of their papers we have counts for
11 papers
Emotional Intelligence of Large Language Models
Xuena Wang, Xueting Li, Zi Yin +2
Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and…
One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration
Yongzhe Yuan, Yue Wu, Maoguo Gong +2
The precision of unsupervised point cloud registration methods is typically limited by the lack of reliable inlier estimation and self-supervised signal, especially in partially ov…
Preserving Commonsense Knowledge from Pre-trained Language Models via Causal Inference
Junhao Zheng, Qianli Ma, Shengjie Qiu +6
Fine-tuning has been proven to be a simple and effective technique to transfer the learned knowledge of Pre-trained Language Models (PLMs) to downstream tasks. However, vanilla fin…
Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents
Yue Wu, So Yeon Min, Yonatan Bisk +5
Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging co…
SPRING: Studying the Paper and Reasoning to Play Games
Yue Wu, Shrimai Prabhumoye, So Yeon Min +5
Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement le…
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
Yue Wu, Yewen Fan, Paul Pu Liang +3
High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructu…
Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration
Wu Yue, Peiran Gong, Maoguo Gong +5
Point cloud registration (PCR) is a popular research topic in computer vision. Recently, the registration method in an evolutionary way has received continuous attention because of…
Bayesian Layer Graph Convolutioanl Network for Hyperspetral Image Classification
Mingyang Zhang, Ziqi Di, Maoguo Gong +3
In recent years, research on hyperspectral image (HSI) classification has continuous progress on introducing deep network models, and recently the graph convolutional network (GCN)…
Towards Consistency and Complementarity: A Multiview Graph Information Bottleneck Approach
Xiaolong Fan, Maoguo Gong, Yue Wu +3
The empirical studies of Graph Neural Networks (GNNs) broadly take the original node feature and adjacency relationship as singleview input, ignoring the rich information of multip…
Multi-view Point Cloud Registration based on Evolutionary Multitasking with Bi-Channel Knowledge Sharing Mechanism
Yue Wu, Yibo Liu, Maoguo Gong +5
Multi-view point cloud registration is fundamental in 3D reconstruction. Since there are close connections between point clouds captured from different viewpoints, registration per…
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph Representations
Xiaolong Fan, Maoguo Gong, Yue Wu +1
Recently, maximizing mutual information has emerged as a powerful method for unsupervised graph representation learning. The existing methods are typically effective to capture inf…