no code implementations • Findings (ACL) 2022 • Hongyuan Lu, Wai Lam, Hong Cheng, Helen Meng
We propose a novel framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable contexts in an end-to-end manner.
1 code implementation • ACL 2022 • Chun Hei Lo, Wai Lam, Hong Cheng
We introduce a data-driven approach to generating derivation trees from meaning representation graphs with probabilistic synchronous hyperedge replacement grammar (PSHRG).
no code implementations • NAACL 2022 • Hongyuan Lu, Wai Lam, Hong Cheng, Helen Meng
Incorporating personas information allows diverse and engaging responses in dialogue response generation.
no code implementations • 13 Mar 2024 • Minbin Huang, Yanxin Long, Xinchi Deng, Ruihang Chu, Jiangfeng Xiong, Xiaodan Liang, Hong Cheng, Qinglin Lu, Wei Liu
However, many of these works face challenges in identifying correct output modalities and generating coherent images accordingly as the number of output modalities increases and the conversations go deeper.
no code implementations • 11 Mar 2024 • Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan
This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan.
no code implementations • 27 Feb 2024 • Hong Cheng, Huan Luo, Zhi Liu, Wei Sun, Weitao Li, Qiyue Li
Due to the fluctuation and intermittency of PV generation, the state gap, arising from time-inconsistent states and exacerbated by imprecisely known system delays, significantly impacts the accuracy of voltage control.
no code implementations • 15 Feb 2024 • Haihong Zhao, Aochuan Chen, Xiangguo Sun, Hong Cheng, Jia Li
In response to this challenge, we propose a novel approach called Graph COordinators for PrEtraining (GCOPE), that harnesses the underlying commonalities across diverse graph datasets to enhance few-shot learning.
2 code implementations • 28 Nov 2023 • Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, Jia Li
This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications.
1 code implementation • 21 Nov 2023 • Yuhan Li, ZHIXUN LI, Peisong Wang, Jia Li, Xiangguo Sun, Hong Cheng, Jeffrey Xu Yu
First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i. e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks.
1 code implementation • 9 Nov 2023 • Xiangguo Sun, Hong Cheng, Hang Dong, Bo Qiao, Si Qin, QIngwei Lin
To establish such scoring systems, several "empirical criteria" are firstly determined, followed by dedicated top-down design for each factor of the score, which usually requires enormous effort to adjust and tune the scoring function in the new application scenario.
no code implementations • 18 Oct 2023 • Qichao Wang, Tian Bian, Yian Yin, Tingyang Xu, Hong Cheng, Helen M. Meng, Zibin Zheng, Liang Chen, Bingzhe Wu
The recent surge in the research of diffusion models has accelerated the adoption of text-to-image models in various Artificial Intelligence Generated Content (AIGC) commercial products.
no code implementations • 15 Sep 2023 • Chun Hei Lo, Wai Lam, Hong Cheng, Guy Emerson
Functional Distributional Semantics (FDS) models the meaning of words by truth-conditional functions.
no code implementations • 17 Aug 2023 • Lu Yang, Zhenglun Kong, Ting Li, Xinyi Bai, Zhiye Lin, Hong Cheng
Traditional image stitching focuses on a single panorama frame without considering the spatial-temporal consistency in videos.
no code implementations • 13 Aug 2023 • Hong Cheng, Rebekah Leamons, Ahmad Al Shami
This paper proposes a modified Topological Data Analysis model for skin images preprocessing and enhancements.
1 code implementation • 18 Jul 2023 • Rui Zhang, Yixin Su, Bayu Distiawan Trisedya, Xiaoyan Zhao, Min Yang, Hong Cheng, Jianzhong Qi
In this paper, we propose the first fully automatic alignment method named AutoAlign, which does not require any manually crafted seed alignments.
no code implementations • 12 Jul 2023 • Xinyi Bai, Ze Wang, Lu Yang, Hong Cheng
The virtual viewpoint is perceived as a new technique in virtual navigation, as yet not supported due to the lack of depth information and obscure camera parameters.
1 code implementation • 4 Jul 2023 • Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan
Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks.
no code implementations • 3 Jun 2023 • Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu
This survey is expected to facilitate researchers' collaborative efforts to tackle the challenges of real-life RE systems.
1 code implementation • 9 May 2023 • Haonan Wang, Minbin Huang, Runhui Huang, Lanqing Hong, Hang Xu, Tianyang Hu, Xiaodan Liang, Zhenguo Li, Hong Cheng, Kenji Kawaguchi
In this work, we present HELIP, a cost-effective strategy tailored to enhance the performance of existing CLIP models without the need for training a model from scratch or collecting additional data.
1 code implementation • 4 Mar 2023 • Tian Bian, Yuli Jiang, Jia Li, Tingyang Xu, Yu Rong, Yi Su, Timothy Kwok, Helen Meng, Hong Cheng
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-drug antagonism can lead to serious adverse effects or even death.
1 code implementation • 18 Feb 2023 • Qiyue Li, Huan Luo, Hong Cheng, Yuxing Deng, Wei Sun, Weitao Li, Zhi Liu
Incipient fault detection in power distribution systems is crucial to improve the reliability of the grid.
no code implementations • 16 Oct 2022 • Rui Zhang, Xiaoyan Zhao, Bayu Distiawan Trisedya, Min Yang, Hong Cheng, Jianzhong Qi
The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity.
no code implementations • 17 Mar 2022 • Jianwei Zhao, Qiang Zhai, Pengbo Zhao, Rui Huang, Hong Cheng
Geolocation is a fundamental component of route planning and navigation for unmanned vehicles, but GNSS-based geolocation fails under denial-of-service conditions.
1 code implementation • 7 Feb 2022 • Yang Deng, Wenxuan Zhang, Wai Lam, Hong Cheng, Helen Meng
In this paper, we propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction, by jointly learning User Satisfaction Estimation and Dialogue Act Recognition tasks.
no code implementations • 27 Nov 2021 • Hongyuan Lu, Wai Lam, Hong Cheng, Helen M. Meng
We incorporate reinforcement learning with a dedicatedly designed critic network for reward judgement.
no code implementations • NeurIPS 2021 • Jia Li, Jiajin Li, Yang Liu, Jianwei Yu, Yueting Li, Hong Cheng
In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct the input graph signal?"
no code implementations • 29 Sep 2021 • Tian Bian, Tingyang Xu, Yu Rong, Wenbing Huang, Xi Xiao, Peilin Zhao, Junzhou Huang, Hong Cheng
Graph Clustering, which clusters the nodes of a graph given its collection of node features and edge connections in an unsupervised manner, has long been researched in graph learning and is essential in certain applications.
1 code implementation • CVPR 2021 • Xin Li, Deng-Ping Fan, Fan Yang, Ao Luo, Hong Cheng, Zicheng Liu
We address this problem with the use of a novel Probabilistic Model Distillation (PMD) approach which transfers knowledge learned by a probabilistic teacher model on synthetic data to a static student model with the use of unlabeled real image pairs.
no code implementations • 8 Apr 2021 • Yuli Jiang, Yu Rong, Hong Cheng, Xin Huang, Kangfei Zhao, Junzhou Huang
In this paper, we propose Graph Neural Network models for both CS and ACS problems, i. e., Query Driven-GNN and Attributed Query Driven-GNN.
1 code implementation • CVPR 2021 • Qiang Zhai, Xin Li, Fan Yang, Chenglizhao Chen, Hong Cheng, Deng-Ping Fan
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models.
1 code implementation • 8 Feb 2021 • Jia Li, Mengzhou Liu, Honglei Zhang, Pengyun Wang, Yong Wen, Lujia Pan, Hong Cheng
We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs.
1 code implementation • 15 Jan 2021 • Mudit Chaudhary, Borislav Dzodzo, Sida Huang, Chun Hei Lo, Mingzhi Lyu, Lun Yiu Nie, Jinbo Xing, Tianhua Zhang, Xiaoying Zhang, Jingyan Zhou, Hong Cheng, Wai Lam, Helen Meng
Dialog systems enriched with external knowledge can handle user queries that are outside the scope of the supporting databases/APIs.
1 code implementation • ICCV 2021 • Fan Yang, Qiang Zhai, Xin Li, Rui Huang, Ao Luo, Hong Cheng, Deng-Ping Fan
Spotting objects that are visually adapted to their surroundings is challenging for both humans and AI.
no code implementations • 22 Dec 2020 • Jia Li, Tomas Yu, Da-Cheng Juan, Arjun Gopalan, Hong Cheng, Andrew Tomkins
Recent studies have indicated that Graph Convolutional Networks (GCNs) act as a \emph{low pass} filter in spectral domain and encode smoothed node representations.
no code implementations • 24 Nov 2020 • Jie Ni, Jiayi Qian, Yixiao Lu, Hong Cheng
Therefore, to improve economic development without compromising the regions' competitiveness in central and western China, we can adjust the power generation structure, and increase investments in the renewable energy supply and energy efficiency.
Applications
1 code implementation • NeurIPS 2020 • Jia Li, Tomasyu Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang
In this work, we present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors.
1 code implementation • ECCV 2020 • Ao Luo, Xin Li, Fan Yang, Zhicheng Jiao, Hong Cheng, Siwei Lyu
Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors. In this work, we introduceCascade Graph Neural Networks(Cas-Gnn), a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection.
Ranked #5 on RGB-D Salient Object Detection on NJU2K
no code implementations • 12 Feb 2020 • Wei Shi, Si-Yuan Zhang, Zhiwei Zhang, Hong Cheng, Jeffrey Xu Yu
The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document.
no code implementations • 31 Jan 2020 • Ao Luo, Fan Yang, Xin Li, Dong Nie, Zhicheng Jiao, Shangchen Zhou, Hong Cheng
In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph.
1 code implementation • 22 Jan 2020 • Jia Li, Honglei Zhang, Zhichao Han, Yu Rong, Hong Cheng, Junzhou Huang
It has been demonstrated that adversarial graphs, i. e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks.
no code implementations • 25 Sep 2019 • Zhichao Han, Jia Li, Xu Li, Hong Cheng
Such linear transformation will result in these good properties.
2 code implementations • 10 May 2019 • Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, Lujia Pan
Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.
1 code implementation • 10 Apr 2019 • Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, Junzhou Huang
We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e. g., a user group in the above example.
Ranked #10 on Graph Classification on D&D
1 code implementation • ECCV 2018 • Xin Li, Fan Yang, Hong Cheng, Wei Liu, Dinggang Shen
Our goal is to overcome this limitation by automatically converting an existing deep contour detection model into a salient object detection model without using any manual salient object masks.
no code implementations • NeurIPS 2017 • Yuanyuan Liu, Fanhua Shang, James Cheng, Hong Cheng, Licheng Jiao
In this paper, we propose an accelerated first-order method for geodesically convex optimization, which is the generalization of the standard Nesterov's accelerated method from Euclidean space to nonlinear Riemannian space.
no code implementations • CVPR 2017 • Fan Yang, Xin Li, Hong Cheng, Jianping Li, Leiting Chen
To address these problems, this paper proposes an object-aware method to estimate per-pixel correspondences from semantic to low-level by learning a classifier for each selected discriminative grid cell and guiding the localization of every pixel under the semantic constraint.
no code implementations • 22 Jun 2016 • Ratha Pech, Dong Hao, Liming Pan, Hong Cheng, Tao Zhou
Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years.
no code implementations • 26 Dec 2015 • Fanhua Shang, James Cheng, Hong Cheng
We first induce the equivalence relation of the Schatten p-norm (0<p<\infty) of a low multi-linear rank tensor and its core tensor.
no code implementations • 2 Jun 2015 • Nan Zhou, Yangyang Xu, Hong Cheng, Jun Fang, Witold Pedrycz
In this paper, we propose a global and local structure preserving sparse subspace learning (GLoSS) model for unsupervised feature selection.
no code implementations • 7 Mar 2015 • Linxiao Yang, Jun Fang, Hong Cheng, Hongbin Li
In this paper, we propose a new hierarchical Bayesian model for dictionary learning, in which a Gaussian-inverse Gamma hierarchical prior is used to promote the sparsity of the representation.
no code implementations • NeurIPS 2014 • Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng
Then the Schatten 1-norm of the core tensor is used to replace that of the whole tensor, which leads to a much smaller-scale matrix SNM problem.
no code implementations • 3 Sep 2014 • Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, Hong Cheng
In this paper, we propose a scalable, provable structured low-rank matrix factorization method to recover low-rank and sparse matrices from missing and grossly corrupted data, i. e., robust matrix completion (RMC) problems, or incomplete and grossly corrupted measurements, i. e., compressive principal component pursuit (CPCP) problems.
no code implementations • 4 Apr 2014 • Chengyu Peng, Hong Cheng, Manchor Ko
There are a large number of methods for solving under-determined linear inverse problem.