no code implementations • 26 Apr 2024 • Yinghan Cheng, Qi Zhang, Chongyang Shi, Liang Xiao, Shufeng Hao, Liang Hu
To address these challenges, we present a novel collaborative stance detection framework called (CoSD) which leverages contrastive heterogeneous topic graph learning to learn topic-aware semantics and collaborative signals among texts, topics, and stance labels for enhancing stance detection.
no code implementations • 20 Apr 2024 • Guangyin Bao, Zixuan Gong, Qi Zhang, Jialei Zhou, Wei Fan, Kun Yi, Usman Naseem, Liang Hu, Duoqian Miao
We meticulously evaluate the performance of our approach across coarse-grained and fine-grained visual decoding tasks.
no code implementations • 19 Apr 2024 • Zixuan Gong, Qi Zhang, Guangyin Bao, Lei Zhu, Ke Liu, Liang Hu, Duoqian Miao
Decoding natural visual scenes from brain activity has flourished, with extensive research in single-subject tasks and, however, less in cross-subject tasks.
no code implementations • 23 Feb 2024 • Kun Yi, Qi Zhang, Hui He, Kaize Shi, Liang Hu, Ning An, Zhendong Niu
Multivariate time series (MTS) forecasting is crucial in many real-world applications.
no code implementations • 18 Feb 2024 • Liang Xiao, Qi Zhang, Chongyang Shi, Shoujin Wang, Usman Naseem, Liang Hu
These existing methods fail to handle the complex, subtle twists in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing.
no code implementations • 3 Feb 2024 • Jianing He, Qi Zhang, Weiping Ding, Duoqian Miao, Jun Zhao, Liang Hu, Longbing Cao
DE$^3$-BERT implements a hybrid exiting strategy that supplements classic entropy-based local information with distance-based global information to enhance the estimation of prediction correctness for more reliable early exiting decisions.
no code implementations • 3 Feb 2024 • Ran Miao, Xueyu Chen, Liang Hu, Zhifei Zhang, Minghua Wan, Qi Zhang, Cairong Zhao
Patent documents in the patent database (PatDB) are crucial for research, development, and innovation as they contain valuable technical information.
no code implementations • 21 Dec 2023 • Guangyin Bao, Qi Zhang, Duoqian Miao, Zixuan Gong, Liang Hu, Ke Liu, Yang Liu, Chongyang Shi
In real-world scenarios, multimodal federated learning often faces the practical challenge of intricate modality missing, which poses constraints on building federated frameworks and significantly degrades model inference accuracy.
1 code implementation • 18 Dec 2023 • An Lao, Qi Zhang, Chongyang Shi, Longbing Cao, Kun Yi, Liang Hu, Duoqian Miao
Multimodal content, such as mixing text with images, presents significant challenges to rumor detection in social media.
no code implementations • 6 Dec 2023 • Zixuan Gong, Qi Zhang, Guangyin Bao, Lei Zhu, Yu Zhang, Ke Liu, Liang Hu, Duoqian Miao
The limited data availability and the low signal-to-noise ratio of fMRI signals lead to the challenging task of fMRI-to-image retrieval.
no code implementations • 17 Sep 2023 • Xiangrui Su, Qi Zhang, Chongyang Shi, Jiachang Liu, Liang Hu
Existing VQA methods integrate vision modeling and language understanding to explore the deep semantics of the question.
no code implementations • 27 Apr 2023 • Qi Zhang, Yayi Yang, Chongyang Shi, An Lao, Liang Hu, Shoujin Wang, Usman Naseem
Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite adhoc event trees called BAET.
no code implementations • 4 Feb 2023 • Kun Yi, Qi Zhang, Longbing Cao, Shoujin Wang, Guodong Long, Liang Hu, Hui He, Zhendong Niu, Wei Fan, Hui Xiong
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
no code implementations • 6 Oct 2022 • Kun Yi, Qi Zhang, Liang Hu, Hui He, Ning An, Longbing Cao, Zhendong Niu
The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements.
no code implementations • 1 Sep 2022 • Xinyu Jiang, Qi Zhang, Chongyang Shi, Kaiying Jiang, Liang Hu, Shoujin Wang
Story ending generation aims at generating reasonable endings for a given story context.
no code implementations • 29 Jun 2022 • Qi Zhang, Liang Hu, Chongyang Shi, Ke Liu, Longbing Cao
Case-based Reasoning (CBR) on high-dimensional and heterogeneous data is a trending yet challenging and computationally expensive task in the real world.
no code implementations • 22 May 2022 • Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, Charu Aggarwal
In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations.
no code implementations • 5 May 2022 • Qi Li, Liang Hu, Jinbo Zhang, Jianping Chen, Guiling Wu
We report on the realization of a long-haul radio frequency (RF) transfer scheme by using multiple-access relay stations (MARSs).
no code implementations • 8 Sep 2021 • Liang Hu, Jiangcheng Zhu, Zirui Zhou, Ruiqing Cheng, Xiaolong Bai, Yong Zhang
Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users with computational resources to train their deep learning jobs.
1 code implementation • 13 May 2021 • Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
no code implementations • 3 Mar 2021 • Liang Hu, Yujie Tang, Zhipeng Zhou, Wei Pan
This paper presents a deep reinforcement learning (DRL) algorithm for orientation estimation using inertial sensors combined with magnetometer.
no code implementations • 26 Oct 2020 • Liang Hu, ChengWei Wu, Wei Pan
An actor-critic reinforcement learning algorithm is proposed to learn the state estimator approximated by a deep neural network.
no code implementations • 30 May 2020 • Shoujin Wang, Longbing Cao, Liang Hu, Shlomo Berkovsky, Xiaoshui Huang, Lin Xiao, Wenpeng Lu
Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item.
no code implementations • 22 Apr 2020 • Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet Orgun, Longbing Cao, Nan Wang, Francesco Ricci, Philip S. Yu
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
no code implementations • 28 Dec 2019 • Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, Mehmet Orgun
The emerging topic of sequential recommender systems has attracted increasing attention in recent years. Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time.
2 code implementations • 9 May 2016 • Liqian Ma, Hong Liu, Liang Hu, Can Wang, Qianru Sun
Experimental results on three public datasets and two proposed datasets demonstrate the superiority of the proposed approach, indicating the effectiveness of body structure and orientation information for improving re-identification performance.