Search Results for author: Hong Cheng

Found 37 papers, 15 papers with code

On Controlling Fallback Responses for Grounded Dialogue Generation

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.

Dialogue Generation Informativeness

Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations

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).

Semantic Composition

Decision Support System for Chronic Diseases Based on Drug-Drug Interactions

1 code implementation4 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.

Representation Learning

TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs

no code implementations16 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.

Entity Alignment Entity Embeddings +1

Co-visual pattern augmented generative transformer learning for automobile geo-localization

no code implementations17 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.

User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems

1 code implementation7 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.

Partner Personas Generation for Diverse Dialogue Generation

no code implementations27 Nov 2021 Hongyuan Lu, Wai Lam, Hong Cheng, Helen M. Meng

We incorporate reinforcement learning with a dedicatedly designed critic network for reward judgement.

Dialogue Generation Response Generation

Deconvolutional Networks on Graph Data

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?"

Graph Learning Imputation

Weakly Supervised Graph Clustering

no code implementations29 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.

Graph Clustering Graph Learning

Probabilistic Model Distillation for Semantic Correspondence

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.

Representation Learning Semantic correspondence

Mutual Graph Learning for Camouflaged Object Detection

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.

Graph Learning object-detection +1

Mask-GVAE: Blind Denoising Graphs via Partition

1 code implementation8 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.


Graph Autoencoders with Deconvolutional Networks

no code implementations22 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.

Graph Generation

The thermal power generation and economic growth in the central and western China: A heterogeneous mixed panel Granger-Causality approach

no code implementations24 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.


Dirichlet Graph Variational Autoencoder

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.

Graph Clustering Graph Generation

Cascade Graph Neural Networks for RGB-D Salient Object Detection

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.

object-detection RGB-D Salient Object Detection +2

Joint Embedding in Named Entity Linking on Sentence Level

no code implementations12 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.

Entity Linking Knowledge Graphs

Hybrid Graph Neural Networks for Crowd Counting

no code implementations31 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.

Crowd Counting

Adversarial Attack on Community Detection by Hiding Individuals

1 code implementation22 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.

Adversarial Attack Community Detection +1

Word embedding re-examined: is the symmetrical factorization optimal?

no code implementations25 Sep 2019 Zhichao Han, Jia Li, Xu Li, Hong Cheng

Such linear transformation will result in these good properties.

Predicting Path Failure In Time-Evolving Graphs

1 code implementation10 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.

Semi-Supervised Graph Classification: A Hierarchical Graph Perspective

1 code implementation10 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.

General Classification Graph Classification +3

Contour Knowledge Transfer for Salient Object Detection

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.

Contour Detection object-detection +3

Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds

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.

Object-Aware Dense Semantic Correspondence

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.

Semantic correspondence

Link Prediction via Matrix Completion

no code implementations22 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.

Link Prediction Matrix Completion

Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning

no code implementations26 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.

Relational Reasoning

Global and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection

no code implementations2 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.

feature selection

Sparse Bayesian Dictionary Learning with a Gaussian Hierarchical Model

no code implementations7 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.

Bayesian Inference Dictionary Learning +1

Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion

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.

Tensor Decomposition

Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations

no code implementations3 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.

Matrix Completion

An Efficient Two-Stage Sparse Representation Method

no code implementations4 Apr 2014 Chengyu Peng, Hong Cheng, Manchor Ko

There are a large number of methods for solving under-determined linear inverse problem.

Vocal Bursts Valence Prediction

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