Zobrazeno 1 - 10
of 211
pro vyhledávání: '"Li Yichuan"'
Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. We intr
Externí odkaz:
http://arxiv.org/abs/2410.07074
Publikováno v:
IEEE BigData 2021
Data augmentation has shown its effectiveness in resolving the data-hungry problem and improving model's generalization ability. However, the quality of augmented data can be varied, especially compared with the raw/original data. To boost deep learn
Externí odkaz:
http://arxiv.org/abs/2409.17474
With the capabilities of understanding and executing natural language instructions, Large language models (LLMs) can potentially act as a powerful tool for textual data augmentation. However, the quality of augmented data depends heavily on the augme
Externí odkaz:
http://arxiv.org/abs/2404.17642
The development of robotic systems for palletization in logistics scenarios is of paramount importance, addressing critical efficiency and precision demands in supply chain management. This paper investigates the application of Reinforcement Learning
Externí odkaz:
http://arxiv.org/abs/2404.04772
Efficiency and reliability are critical in robotic bin-picking as they directly impact the productivity of automated industrial processes. However, traditional approaches, demanding static objects and fixed collisions, lead to deployment limitations,
Externí odkaz:
http://arxiv.org/abs/2403.16786
Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities, where a LLM makes predictions for a given test input together with a few input-output pairs (demonstrations). Nevertheless, the inclusion of demonstrati
Externí odkaz:
http://arxiv.org/abs/2403.06914
Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received increasing research attention lately. However, existing methods either str
Externí odkaz:
http://arxiv.org/abs/2310.15109
In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. On entity-rich textual resources like Wikipedia, Knowledge-Enhanced PLMs (KEPLMs) i
Externí odkaz:
http://arxiv.org/abs/2305.01810
Autor:
Cao, Hanwen, Zhou, Jianshu, Huang, Junda, Li, Yichuan, Meng, Ng Cheng, Cao, Rui, Dou, Qi, Liu, Yunhui
This paper proposes a novel bin picking framework, two-stage grasping, aiming at precise grasping of cluttered small objects. Object density estimation and rough grasping are conducted in the first stage. Fine segmentation, detection, grasping, and p
Externí odkaz:
http://arxiv.org/abs/2303.02604
Heterogeneous networks comprise agents with varying capabilities in terms of computation, storage, and communication. In such settings, it is crucial to factor in the operating characteristics in allowing agents to choose appropriate updating schemes
Externí odkaz:
http://arxiv.org/abs/2209.01276