no code implementations • 13 Feb 2024 • Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo, Jiliang Tang, Yi Chang
These insights have empowered us to develop a novel GNN backbone model, DGAT, designed to harness the robust properties of both graph self-attention mechanism and the decoupled architecture.
no code implementations • 31 Jan 2024 • Qingmin Jia, Yujiao Hu, Xiaomao Zhou, Qianpiao Ma, Kai Guo, Huayu Zhang, Renchao Xie, Tao Huang, Yunjie Liu
With the development of new Internet services such as computation-intensive and delay-sensitive tasks, the traditional "Best Effort" network transmission mode has been greatly challenged.
no code implementations • 13 Jul 2023 • David James Woo, Hengky Susanto, Kai Guo
This study applies Activity Theory and investigates the attitudes and contradictions of 67 English as a foreign language (EFL) students from four Hong Kong secondary schools towards machine-in-the-loop writing, where artificial intelligence (AI) suggests ideas during composition.
no code implementations • 19 Jun 2023 • David James Woo, Kai Guo, Hengky Susanto
ChatGPT is a state-of-the-art (SOTA) chatbot.
no code implementations • 1 Jun 2023 • David James Woo, Kai Guo, Hengky Susanto
The study also identified common characteristics of students' activity systems, including the sophistication of their generative-AI tools, the quality of their stories, and their school's overall academic achievement level, for their prompting of generative-AI tools for the three purposes during short story writing.
no code implementations • 21 Apr 2023 • Hengky Susanto, David James Woo, Kai Guo
The recent advancement in Natural Language Processing (NLP) capability has led to the development of language models (e. g., ChatGPT) that is capable of generating human-like language.
no code implementations • 10 Mar 2023 • David James Woo, Hengky Susanto, Chi Ho Yeung, Kai Guo, April Ka Yeng Fung
Human experts scored the stories for dimensions of content, language and organization.
no code implementations • 20 Dec 2022 • YuQi Yang, Songyun Yang, Jiyang Xie. Zhongwei Si, Kai Guo, Ke Zhang, Kongming Liang
We adopt a multi-head architecture with multiple prediction heads (i. e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI.
no code implementations • 17 Oct 2022 • Kai Guo, Seungwon Choi, Jongseong Choi
First, the reset gate is employed to mark the content related to the current frame in the previous frame output.
no code implementations • 4 Jun 2022 • David James Woo, Yanzhi Wang, Hengky Susanto, Kai Guo
This study explores strategies adopted by EFL students when searching for ideas using NLG tools, evaluating ideas generated by NLG tools and selecting NLG tools for ideas generation.
no code implementations • 31 Mar 2022 • Weizhi Lu, Mingrui Chen, Kai Guo, Weiyu Li
Furthermore, this quantization property could be maintained in the random projections of sparse features, if both the features and random projection matrices are sufficiently sparse.
no code implementations • 24 Jan 2022 • Liqiang Zhang, Kai Guo, Yu Liu
Kalman filter-based Inertial Navigation System (INS) is a reliable and efficient method to estimate the position of a pedestrian indoors.
no code implementations • 28 Oct 2021 • Haotian Xue, Kaixiong Zhou, Tianlong Chen, Kai Guo, Xia Hu, Yi Chang, Xin Wang
In this paper, we investigate GNNs from the lens of weight and feature loss landscapes, i. e., the loss changes with respect to model weights and node features, respectively.
no code implementations • 20 Oct 2021 • Weizhi Lu, Mingrui Chen, Kai Guo, Weiyu Li
In the letter, we show that target propagation could be achieved by modeling the network s each layer with compressed sensing, without the need of auxiliary networks.
1 code implementation • 23 Sep 2021 • Kai Guo, Kaixiong Zhou, Xia Hu, Yu Li, Yi Chang, Xin Wang
Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations.
no code implementations • 8 Sep 2019 • Kai Guo, Seongwook Song, Soonkeun Chang, Tae-ui Kim, Seungmin Han, Irina Kim
In this paper, to address the above problems we propose a hierarchical hourglass network for robust full-FoV depth estimation in tele-wide camera system, which combines the robustness of traditional stereo-matching methods with the accuracy of DNN.