no code implementations • 25 Sep 2014 • Liang Wu, Hui Xiong, Liang Du, Bo Liu, Guandong Xu, Yong Ge, Yanjie Fu, Yuanchun Zhou, Jianhui Li
Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.
no code implementations • 17 Aug 2016 • Liang Wu, Fred Morstatter, Huan Liu
To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content.
2 code implementations • ASONAM 2019 2019 • Jundong Li, Liang Wu, Huan Liu
As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.
no code implementations • 20 Dec 2018 • Xiao Chen, Julian Cheng, Zaichen Zhang, Liang Wu, Jian Dang
The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance.
Information Theory Information Theory
no code implementations • 10 May 2019 • Wenjie Hu, Jianping Huang, Liang Wu, Yang Yang, Zongtao Liu, Zhanlin Sun, Bingshen Yao, Ke Chen
The modeling of time series is becoming increasingly critical in a wide variety of applications.
2 code implementations • 8 Aug 2019 • Liang Wu, Chengquan Zhang, Jiaming Liu, Junyu Han, Jingtuo Liu, Errui Ding, Xiang Bai
Specifically, we propose an end-to-end trainable style retention network (SRNet) that consists of three modules: text conversion module, background inpainting module and fusion module.
Ranked #1 on Image Inpainting on StreetView
no code implementations • 29 Apr 2021 • Liang Wu, Antoine Ledent, Yunwen Lei, Marius Kloft
In this paper, we initiate the generalization analysis of regularized vector-valued learning algorithms by presenting bounds with a mild dependency on the output dimension and a fast rate on the sample size.
Extreme Multi-Label Classification General Classification +2
no code implementations • 21 Jul 2021 • Didan Deng, Liang Wu, Bertram E. Shi
Iterative distillation over multiple generations significantly improves performance in both emotion recognition and uncertainty estimation.
no code implementations • 17 Aug 2021 • Puyu Wang, Liang Wu, Yunwen Lei
Randomized coordinate descent (RCD) is a popular optimization algorithm with wide applications in solving various machine learning problems, which motivates a lot of theoretical analysis on its convergence behavior.
no code implementations • 4 Dec 2021 • Yuhao Qi, Jian Dang, Zaichen Zhang, Liang Wu, Yongpeng Wu
However, existing works show that there remains residual interference after single-tap equalization even with infinite number of BS antennas, leading to a limitation of achievable signal-to-interference-plus-noise ratio (SINR) performance.
no code implementations • 6 May 2022 • Jian Dang, Zaichen Zhang, Yewei Li, Liang Wu, Bingcheng Zhu, Lei Wang
Reconfigurable intelligent surface (RIS) can assist terahertz wireless communication to restore the fragile line-of-sight links and facilitate beam steering.
no code implementations • 1 Jun 2022 • Pengyuan Lyu, Chengquan Zhang, Shanshan Liu, Meina Qiao, Yangliu Xu, Liang Wu, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang
Specifically, we transform text data into synthesized text images to unify the data modalities of vision and language, and enhance the language modeling capability of the sequence decoder using a proposed masked image-language modeling scheme.
no code implementations • 26 Aug 2022 • Qinyi Zhu, Liang Wu, Qi Guo, Liangjie Hong
Introducing a brand new workplace type naturally leads to the cold-start problem, which is particularly more common for less active job seekers.
no code implementations • 31 Aug 2022 • Liang Wu, Maarten Nauta
The gPROMS-MPC tool implements our previous construction-free CDAL and the online parametric active-set qpOASES algorithms to solve sparse or condensed MPC problem formulations, respectively, for possible successive linearization or high state-dimension cases.
no code implementations • 31 Aug 2022 • Liang Wu
Two kinds of control-oriented models used in MPC are the state-space (SS) model and the input-output model (such as the ARX model).
1 code implementation • 1 Sep 2022 • Yan Xiang, Yu-Hang Tang, Zheng Gong, Hongyi Liu, Liang Wu, Guang Lin, Huai Sun
We introduce an explorative active learning (AL) algorithm based on Gaussian process regression and marginalized graph kernel (GPR-MGK) to explore chemical space with minimum cost.
no code implementations • 4 Sep 2022 • Liang Wu
This interpretative ARX model is then updated online by the EKF algorithm, which is modified as a decoupled one without matrix-inverse operator.
1 code implementation • 9 May 2023 • Yini Fang, Liang Wu, Frederic Jumelle, Bertram Shi
We handle variable video length with a regression token that accumulates information from all frames into a fixed-dimensional vector independent of video length.
1 code implementation • 9 May 2023 • Yini Fang, Didan Deng, Liang Wu, Frederic Jumelle, Bertram Shi
In comparison to optical flow, phase provides more localized motion estimates, which are essential for ME spotting, resulting in higher performance.
1 code implementation • 5 Jun 2023 • Yaochen Zhu, Jing Ma, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
But since sensitive features may also affect user interests in a fair manner (e. g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities.
no code implementations • 26 Jun 2023 • Liang Wu, Richard D. Braatz
Providing an execution time certificate is a pressing requirement when deploying Model Predictive Control (MPC) in real-time embedded systems such as microcontrollers.
no code implementations • 13 Jul 2023 • Wenzhou Lv, Tianyu Wu, Luolin Xiong, Liang Wu, Jian Zhou, Yang Tang, Feng Qian
Objective: The artificial pancreas (AP) has shown promising potential in achieving closed-loop glucose control for individuals with type 1 diabetes mellitus (T1DM).
no code implementations • 16 Jul 2023 • Baiping Xiong, Zaichen Zhang, Yingmeng Ge, Haibo Wang, Hao Jiang, Liang Wu, Ziyang Zhang
In this paper, we consider the channel modeling of a heterogeneous vehicular integrated sensing and communication (ISAC) system, where a dual-functional multi-antenna base station (BS) intends to communicate with a multi-antenna vehicular receiver (MR) and sense the surrounding environments simultaneously.
1 code implementation • 9 Oct 2023 • Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, Yang Yang
This synergy equips LLMs with the ability to proficiently interpret and reason on graph data, harnessing the superior expressive power of graph learning models.
1 code implementation • 2 Nov 2023 • Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model user/item collaborative and content semantics.
no code implementations • 5 Dec 2023 • Xiaohua Xing, Yuqi Ren, Die Zou, Qiankun Zhang, Bingxuan Mao, Jianquan Yao, Deyi Xiong, Shuang Zhang, Liang Wu
Recently, artificial intelligence has been extensively deployed across various scientific disciplines, optimizing and guiding the progression of experiments through the integration of abundant datasets, whilst continuously probing the vast theoretical space encapsulated within the data.
no code implementations • 11 Dec 2023 • Haotian Zhang, Semujju Stuart Dereck, Zhicheng Wang, Xianwei Lv, Kang Xu, Liang Wu, Ye Jia, Jing Wu, Zhuo Long, Wensheng Liang, X. G. Ma, Ruiyan Zhuang
Although the applications of artificial intelligence especially deep learning had greatly improved various aspects of intelligent manufacturing, they still face challenges for wide employment due to the poor generalization ability, difficulties to establish high-quality training datasets, and unsatisfactory performance of deep learning methods.
1 code implementation • 14 Feb 2024 • Xinyuan Wang, Liang Wu, Liangjie Hong, Hao liu, Yanjie Fu
Additionally, we introduce graph relationship understanding and analysis functions into LLMs to enhance their focus on connectivity information in graph data.
no code implementations • 20 Feb 2024 • Ping Liu, Haichao Wei, Xiaochen Hou, Jianqiang Shen, Shihai He, Kay Qianqi Shen, Zhujun Chen, Fedor Borisyuk, Daniel Hewlett, Liang Wu, Srikant Veeraraghavan, Alex Tsun, Chengming Jiang, Wenjing Zhang
This methodology decouples the training of the GNN model from that of existing Deep Neural Nets (DNN) models, eliminating the need for frequent GNN retraining while maintaining up-to-date graph signals in near realtime, allowing for the effective integration of GNN insights through transfer learning.
no code implementations • 25 Feb 2024 • Liang Wu, Krystian Ganko, Shimin Wang, Richard D. Braatz
The execution-time certified capability of the algorithm is theoretically and numerically validated through a case study involving nonlinear control of the chaotic Lorenz system.
no code implementations • 14 Mar 2024 • Liang Wu, X. -G. Ma
We proposed a method of converting hand action recognition problems into hand skeletal trajectory classification problems, which solved the real-time performance problem of industrial algorithms.
no code implementations • 27 Mar 2024 • Liang Wu, Richard D. Braatz
Providing an execution time certificate and handling possible infeasibility in closed-loop are two pressing requirements of Model Predictive Control (MPC).