no code implementations • TACL 2014 • Yuan Fang, Ming-Wei Chang
Microblogs present an excellent opportunity for monitoring and analyzing world happenings.
1 code implementation • 17 Jun 2017 • Zhe Wang, Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Sibo Song, Yuan Fang, Seokhwan Kim, Nancy Chen, Luis Fernando D'Haro, Luu Anh Tuan, Hongyuan Zhu, Zeng Zeng, Ngai Man Cheung, Georgios Piliouras, Jie Lin, Vijay Chandrasekhar
Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.
1 code implementation • The Twenty-Eighth International Joint Conference on Artificial Intelligence Conference 2019 • Duc-Trong Le, Hady W. Lauw, Yuan Fang
Items adopted by a user over time are indicative of the underlying preferences.
no code implementations • 25 Sep 2019 • Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Steven C.H. Hoi.
Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.
3 code implementations • 17 May 2020 • Sezin Kircali Ata, Yuan Fang, Min Wu, Jiaqi Shi, Chee Keong Kwoh, Xiao-Li Li
Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes.
2 code implementations • 15 Jun 2020 • Mandeep Singh, Yuan Fang
Humans are able to comprehend information from multiple domains for e. g. speech, text and visual.
no code implementations • ECCV 2020 • Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks.
1 code implementation • 19 Jul 2020 • Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh, Xiao-Li Li
Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases.
no code implementations • 3 Sep 2020 • Yuan Fang, Chunyan Xu, Zhen Cui, Yuan Zong, Jian Yang
In this paper, we propose a spatial transformer point convolution (STPC) method to achieve anisotropic convolution filtering on point clouds.
no code implementations • 23 Nov 2020 • Peng Li, Baijiang Lv, Yuan Fang, Wei Guo, Zhongzheng Wu, Yi Wu, Cheng-Maw Cheng, Dawei Shen, Yuefeng Nie, Luca Petaccia, Chao Cao, Zhu-An Xu, Yang Liu
Using angle-resolved photoemission spectroscopy (ARPES) and low-energy electron diffraction (LEED), together with density-functional theory (DFT) calculation, we report the formation of charge density wave (CDW) and its interplay with the Kondo effect and topological states in CeSbTe.
Strongly Correlated Electrons Materials Science
no code implementations • 10 Dec 2020 • Yung-Kun Liu, Pisin Chen, Yuan Fang, Petr Valenta
Flying plasma mirrors induced by intense lasers has been proposed as a promising way to generate few-cycle EUV or X-ray lasers.
Plasma Physics
no code implementations • 1 Jan 2021 • Wenting Zhao, Yuan Fang, Zhen Cui, Tong Zhang, Jian Yang, Wei Liu
In this paper, we propose a simple yet effective graph deformer network (GDN) to fulfill anisotropic convolution filtering on graphs, analogous to the standard convolution operation on images.
no code implementations • 1 Jan 2021 • Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven Hoi
Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.
no code implementations • 21 Jan 2021 • Yuan Fang, Ding Wang, Peng Li, Hang Su, Tian Le, Yi Wu, Guo-Wei Yang, Hua-Li Zhang, Zhi-Guang Xiao, Yan-Qiu Sun, Si-Yuan Hong, Yan-Wu Xie, Huan-Hua Wang, Chao Cao, Xin Lu, Hui-Qiu Yuan, Yang Liu
We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).
Mesoscale and Nanoscale Physics
no code implementations • 14 May 2021 • Zhihao Wen, Yuan Fang, Zemin Liu
That is, MI-GNN does not directly learn an inductive model; it learns the general knowledge of how to train a model for semi-supervised node classification on new graphs.
no code implementations • 29 Sep 2021 • Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang
At the graph level, we modulate the graph representation conditioned on the query subgraph, so that the model can be adapted to each unique query for better matching with the input graph.
1 code implementation • 27 Oct 2021 • Zemin Liu, Yuan Fang, Chenghao Liu, Steven C. H. Hoi
Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model shared by all nodes.
1 code implementation • 30 Dec 2021 • Xuexin Chen, Ruichu Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao
However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing \emph{high-order} graph structures as opposed to \emph{low-order} structures.
no code implementations • 14 Dec 2022 • Ruichu Cai, Yuxuan Zhu, Xuexin Chen, Yuan Fang, Min Wu, Jie Qiao, Zhifeng Hao
To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose a framework of Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound.
no code implementations • 15 Dec 2022 • Yuanzhi Cai, Lei Fan, Yuan Fang
However, it was found in this study that different classes of objects had their preferred resizing scale for more accurate semantic segmentation.
no code implementations • 9 Jan 2023 • Huanyu Bian, Zhilong Jia, Menghan Dou, Yuan Fang, Lei LI, Yiming Zhao, Hanchao Wang, Zhaohui Zhou, Wei Wang, Wenyu Zhu, Ye Li, Yang Yang, Weiming Zhang, Nenghai Yu, Zhaoyun Chen, Guoping Guo
Therefore, based on VQNet 1. 0, we further propose VQNet 2. 0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization.
1 code implementation • 7 Feb 2023 • Xingtong Yu, Zemin Liu, Yuan Fang, Xinming Zhang
However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes, which is inadequate in complex structure matching for isomorphism counting.
1 code implementation • 8 Feb 2023 • Zemin Liu, Trung-Kien Nguyen, Yuan Fang
In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes.
no code implementations • 11 Feb 2023 • Deyu Bo, Xiao Wang, Yang Liu, Yuan Fang, Yawen Li, Chuan Shi
Graph neural networks (GNNs) have attracted considerable attention from the research community.
2 code implementations • 16 Feb 2023 • Zemin Liu, Xingtong Yu, Yuan Fang, Xinming Zhang
In particular, prompting is a popular alternative to fine-tuning in natural language processing, which is designed to narrow the gap between pre-training and downstream objectives in a task-specific manner.
1 code implementation • 21 Feb 2023 • Trung-Kien Nguyen, Zemin Liu, Yuan Fang
Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed.
no code implementations • 5 May 2023 • Zhihao Wen, Yuan Fang
Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions.
no code implementations • 8 May 2023 • Yuanzhi Cai, Jagannath Aryal, Yuan Fang, Hong Huang, Lei Fan
In this study, the concept of pruning from a supernet is used for the first time to integrate the selection of channel combination and the training of a semantic segmentation network.
no code implementations • 21 Jun 2023 • Zimeng Li, Sa Xiao, Cheng Wang, Haidong Li, Xiuchao Zhao, Caohui Duan, Qian Zhou, Qiuchen Rao, Yuan Fang, Junshuai Xie, Lei Shi, Fumin Guo, Chaohui Ye, Xin Zhou
Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications.
no code implementations • 26 Jun 2023 • Chuhan Wu, Jingjie Li, Qinglin Jia, Hong Zhu, Yuan Fang, Ruiming Tang
Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications.
no code implementations • 1 Jul 2023 • Yuan Fang, Yuanzhi Cai, Lei Fan
Pansharpening is a process of fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image to create a high-resolution multispectral image.
1 code implementation • 15 Jul 2023 • Zhihao Wen, Yuan Fang
During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore handcrafted discrete prompts and continuous prompt tuning for the jointly pre-trained model to achieve zero- and few-shot classification, respectively.
1 code implementation • 8 Aug 2023 • Dongyang Yu, Shihao Wang, Yuan Fang, Wangpeng An
This paper presents OmniDataComposer, an innovative approach for multimodal data fusion and unlimited data generation with an intent to refine and uncomplicate interplay among diverse data modalities.
Ranked #10 on Zero-Shot Video Question Answer on MSRVTT-QA
Automatic Speech Recognition Automatic Speech Recognition (ASR) +6
no code implementations • 19 Aug 2023 • Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao
We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap.
no code implementations • 29 Aug 2023 • Hong Zhu, Runpeng Yu, Xing Tang, Yifei Wang, Yuan Fang, Yisen Wang
Data in the real-world classification problems are always imbalanced or long-tailed, wherein the majority classes have the most of the samples that dominate the model training.
no code implementations • 18 Oct 2023 • Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains.
2 code implementations • 26 Nov 2023 • Xingtong Yu, Zhenghao Liu, Yuan Fang, Zemin Liu, Sihong Chen, Xinming Zhang
In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs.
1 code implementation • 28 Nov 2023 • Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhang
Hence, in this paper, we propose MultiGPrompt, a novel multi-task pre-training and prompting framework to exploit multiple pretext tasks for more comprehensive pre-trained knowledge.
no code implementations • 4 Dec 2023 • Xingtong Yu, Yuan Fang, Zemin Liu, Xinming Zhang
In this paper, we propose HGPROMPT, a novel pre-training and prompting framework to unify not only pre-training and downstream tasks but also homogeneous and heterogeneous graphs via a dual-template design.
no code implementations • 2 Feb 2024 • Xingtong Yu, Yuan Fang, Zemin Liu, Yuxia Wu, Zhihao Wen, Jianyuan Bo, Xinming Zhang, Steven C. H. Hoi
Finally, we outline prospective future directions for few-shot learning on graphs to catalyze continued innovation in this field.
no code implementations • 19 Feb 2024 • Zhihao Wen, Jie Zhang, Yuan Fang
Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time.
no code implementations • 14 Mar 2024 • Yuan Fang, Yipeng Liu, Jie Chen, Zhen Long, Ao Li, Chong-Yung Chi, Ce Zhu
In recent years, the fusion of high spatial resolution multispectral image (HR-MSI) and low spatial resolution hyperspectral image (LR-HSI) has been recognized as an effective method for HSI super-resolution (HSI-SR).
1 code implementation • 25 Mar 2024 • Trung-Kien Nguyen, Yuan Fang
Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space.
1 code implementation • 2 Apr 2024 • Qinfeng Zhu, Yuanzhi Cai, Yuan Fang, Yihan Yang, Cheng Chen, Lei Fan, Anh Nguyen
The results reveal that Samba achieved unparalleled performance on commonly used remote sensing datasets for semantic segmentation.
no code implementations • 12 Apr 2024 • Peijie Sun, Yifan Wang, Min Zhang, Chuhan Wu, Yan Fang, Hong Zhu, Yuan Fang, Meng Wang
In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming.