Search Results for author: Chao Huang

Found 93 papers, 49 papers with code

Graph Transformer for Recommendation

1 code implementation4 Jun 2023 Chaoliu Li, Lianghao Xia, Xubin Ren, Yaowen Ye, Yong Xu, Chao Huang

This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture.

Collaborative Filtering Data Augmentation +3

Denoised Self-Augmented Learning for Social Recommendation

1 code implementation22 May 2023 Tianle Wang, Lianghao Xia, Chao Huang

Social recommendation is gaining increasing attention in various online applications, including e-commerce and online streaming, where social information is leveraged to improve user-item interaction modeling.

Self-Supervised Learning Transfer Learning

Adaptive Graph Contrastive Learning for Recommendation

1 code implementation18 May 2023 Yangqin Jiang, Chao Huang, Lianghao Xia

Two generators are able to create adaptive contrastive views, addressing the problem of model collapse and achieving adaptive contrastive learning.

Collaborative Filtering Contrastive Learning +4

Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

no code implementations12 May 2023 Long Chen, Yuchen Li, Chao Huang, Yang Xing, Daxin Tian, Li Li, Zhongxu Hu, Siyu Teng, Chen Lv, Jinjun Wang, Dongpu Cao, Nanning Zheng, Fei-Yue Wang

Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions.

Autonomous Driving Ethics

Graph Masked Autoencoder for Sequential Recommendation

2 code implementations8 May 2023 Yaowen Ye, Lianghao Xia, Chao Huang

While some powerful neural network architectures (e. g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios.

Contrastive Learning Data Augmentation +1

Automated Spatio-Temporal Graph Contrastive Learning

1 code implementation6 May 2023 Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, SiuMing Yiu

In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources.

Contrastive Learning

Disentangled Contrastive Collaborative Filtering

1 code implementation4 May 2023 Xubin Ren, Lianghao Xia, Jiashu Zhao, Dawei Yin, Chao Huang

Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF).

Collaborative Filtering Contrastive Learning +1

Information Recovery-Driven Deep Incomplete Multi-view Clustering Network

2 code implementations2 Apr 2023 Chengliang Liu, Jie Wen, Zhihao Wu, Xiaoling Luo, Chao Huang, Yong Xu

Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data.

Graph Reconstruction Incomplete multi-view clustering +1

POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems

no code implementations31 Mar 2023 YiXuan Wang, Weichao Zhou, Jiameng Fan, Zhilu Wang, Jiajun Li, Xin Chen, Chao Huang, Wenchao Li, Qi Zhu

We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.

Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys

no code implementations30 Mar 2023 Long Chen, Yuchen Li, Chao Huang, Bai Li, Yang Xing, Daxin Tian, Li Li, Zhongxu Hu, Xiaoxiang Na, Zixuan Li, Siyu Teng, Chen Lv, Jinjun Wang, Dongpu Cao, Nanning Zheng, Fei-Yue Wang

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits.

Autonomous Driving Ethics

Egocentric Audio-Visual Object Localization

1 code implementation CVPR 2023 Chao Huang, Yapeng Tian, Anurag Kumar, Chenliang Xu

In this paper, we explore the challenging egocentric audio-visual object localization task and observe that 1) egomotion commonly exists in first-person recordings, even within a short duration; 2) The out-of-view sound components can be created while wearers shift their attention.

Object Localization

DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification

2 code implementations15 Mar 2023 Chengliang Liu, Jie Wen, Xiaoling Luo, Chao Huang, Zhihao Wu, Yong Xu

To deal with the double incomplete multi-view multi-label classification problem, we propose a deep instance-level contrastive network, namely DICNet.

Contrastive Learning Multi-Label Classification +1

Graph-less Collaborative Filtering

1 code implementation15 Mar 2023 Lianghao Xia, Chao Huang, Jiao Shi, Yong Xu

Motivated by these limitations, we propose a simple and effective collaborative filtering model (SimRec) that marries the power of knowledge distillation and contrastive learning.

Collaborative Filtering Contrastive Learning +2

Automated Self-Supervised Learning for Recommendation

1 code implementation14 Mar 2023 Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, Ben Kao

This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation.

Collaborative Filtering Contrastive Learning +2

Disentangled Graph Social Recommendation

1 code implementation14 Mar 2023 Lianghao Xia, Yizhen Shao, Chao Huang, Yong Xu, Huance Xu, Jian Pei

In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections.

Recommendation Systems

Heterogeneous Graph Contrastive Learning for Recommendation

1 code implementation2 Mar 2023 Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, Ronghua Luo

In light of this, we propose a Heterogeneous Graph Contrastive Learning (HGCL), which is able to incorporate heterogeneous relational semantics into the user-item interaction modeling with contrastive learning-enhanced knowledge transfer across different views.

Contrastive Learning Recommendation Systems +3

Multi-Modal Self-Supervised Learning for Recommendation

1 code implementation21 Feb 2023 Wei Wei, Chao Huang, Lianghao Xia, Chuxu Zhang

The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering personalized recommender systems to incorporate various modalities (eg, visual, textual and acoustic) into the latent user representations.

Contrastive Learning Data Augmentation +2

Multi-Behavior Graph Neural Networks for Recommender System

no code implementations17 Feb 2023 Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo

Recent years have witnessed the emerging success of many deep learning-based recommendation models for augmenting collaborative filtering architectures with various neural network architectures, such as multi-layer perceptron and autoencoder.

Collaborative Filtering Recommendation Systems +1

LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation

1 code implementation16 Feb 2023 Xuheng Cai, Chao Huang, Lianghao Xia, Xubin Ren

In this paper, we propose a simple yet effective graph contrastive learning paradigm LightGCL that mitigates these issues impairing the generality and robustness of CL-based recommenders.

Contrastive Learning Data Augmentation +1

Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks

no code implementations11 Feb 2023 Wei Chen, Chao Huang, Yanwei Yu, Yongguo Jiang, Junyu Dong

Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking different trajectories to users with the exploration of complex mobility patterns.

AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis

no code implementations4 Feb 2023 Susan Liang, Chao Huang, Yapeng Tian, Anurag Kumar, Chenliang Xu

Human perception of the complex world relies on a comprehensive analysis of multi-modal signals, and the co-occurrences of audio and video signals provide humans with rich cues.

Audio Generation Data Augmentation

Do We Really Need Graph Neural Networks for Traffic Forecasting?

no code implementations30 Jan 2023 Xu Liu, Yuxuan Liang, Chao Huang, Hengchang Hu, Yushi Cao, Bryan Hooi, Roger Zimmermann

Spatio-temporal graph neural networks (STGNN) have become the most popular solution to traffic forecasting.

CIGAR: Cross-Modality Graph Reasoning for Domain Adaptive Object Detection

no code implementations CVPR 2023 Yabo Liu, Jinghua Wang, Chao Huang, YaoWei Wang, Yong Xu

To overcome these problems, we propose a cross-modality graph reasoning adaptation (CIGAR) method to take advantage of both visual and linguistic knowledge.

Graph Matching object-detection +1

Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View Clustering

1 code implementation CVPR 2023 Jie Wen, Chengliang Liu, Gehui Xu, Zhihao Wu, Chao Huang, Lunke Fei, Yong Xu

Graph-based multi-view clustering has attracted extensive attention because of the powerful clustering-structure representation ability and noise robustness.

Graph Learning Incomplete multi-view clustering

Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction

1 code implementation7 Dec 2022 Jiahao Ji, Jingyuan Wang, Chao Huang, Junjie Wu, Boren Xu, Zhenhe Wu, Junbo Zhang, Yu Zheng

ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods.

Robust Traffic Prediction Self-Supervised Learning +2

Quantifying the Impact of Label Noise on Federated Learning

no code implementations15 Nov 2022 Shuqi Ke, Chao Huang, Xin Liu

Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets.

Federated Learning

Firm-worker hypergraphs

no code implementations13 Nov 2022 Chao Huang

A firm-worker hypergraph consists of edges in which each edge joins a firm and its possible staff.

Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning

no code implementations1 Oct 2022 Chunhui Zhang, Chao Huang, Yijun Tian, Qianlong Wen, Zhongyu Ouyang, Youhuan Li, Yanfang Ye, Chuxu Zhang

The effectiveness is further guaranteed and proved by the gradients' distance between the subset and the full set; (ii) empirically, we discover that during the learning process of a GNN, some samples in the training dataset are informative for providing gradients to update model parameters.

Contrastive Learning Graph Representation Learning

Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments

no code implementations29 Sep 2022 YiXuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu

It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state not to reach certain specified unsafe regions.

Reinforcement Learning (RL) Safe Reinforcement Learning

A Tool for Neural Network Global Robustness Certification and Training

no code implementations15 Aug 2022 Zhilu Wang, YiXuan Wang, Feisi Fu, Ruochen Jiao, Chao Huang, Wenchao Li, Qi Zhu

Moreover, GROCET provides differentiable global robustness, which is leveraged in the training of globally robust neural networks.

Multiplex Heterogeneous Graph Convolutional Network

1 code implementation12 Aug 2022 Pengyang Yu, Chaofan Fu, Yanwei Yu, Chao Huang, Zhongying Zhao, Junyu Dong

Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification.

Link Prediction Network Embedding +1

Localized Sparse Incomplete Multi-view Clustering

1 code implementation5 Aug 2022 Chengliang Liu, Zhihao Wu, Jie Wen, Chao Huang, Yong Xu

Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation.

Graph Embedding Incomplete multi-view clustering

Self-Supervised Hypergraph Transformer for Recommender Systems

1 code implementation28 Jul 2022 Lianghao Xia, Chao Huang, Chuxu Zhang

With the distilled global context, a cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph, so as to enhance the robustness of recommender systems.

Collaborative Filtering Data Augmentation +2

Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation

1 code implementation12 Jul 2022 Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li

Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework.

Sequential Recommendation

Cross-Silo Federated Learning: Challenges and Opportunities

no code implementations26 Jun 2022 Chao Huang, Jianwei Huang, Xin Liu

Federated learning (FL) is an emerging technology that enables the training of machine learning models from multiple clients while keeping the data distributed and private.

Federated Learning

Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

1 code implementation6 Jun 2022 Lianghao Xia, Chao Huang, Yong Xu, Jian Pei

The new TGT method endows the sequential recommendation architecture to distill dedicated knowledge for type-specific behavior relational context and the implicit behavior dependencies.

Sequential Recommendation

Two-sided matching with firms' complementary preferences

no code implementations11 May 2022 Chao Huang

This paper studies two-sided many-to-one matching in which firms have complementary preferences.

Vocal Bursts Valence Prediction

Mutual Distillation Learning Network for Trajectory-User Linking

1 code implementation8 May 2022 Wei Chen, Shuzhe Li, Chao Huang, Yanwei Yu, Yongguo Jiang, Junyu Dong

In this paper, we propose a novel Mutual distillation learning network to solve the TUL problem for sparse check-in mobility data, named MainTUL.

Knowledge Graph Contrastive Learning for Recommendation

1 code implementation2 May 2022 Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li

However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities.

Contrastive Learning General Knowledge +2

Hypergraph Contrastive Collaborative Filtering

1 code implementation26 Apr 2022 Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, Jimmy Xiangji Huang

Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination.

Collaborative Filtering Contrastive Learning +2

Scalable Motif Counting for Large-scale Temporal Graphs

1 code implementation20 Apr 2022 Zhongqiang Gao, Chuanqi Cheng, Yanwei Yu, Lei Cao, Chao Huang, Junyu Dong

We first categorize the temporal motifs based on their distinct properties, and then design customized algorithms that offer efficient strategies to exactly count the motif instances of each category.

Anomaly Detection Representation Learning

Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction

1 code implementation18 Apr 2022 Zhonghang Li, Chao Huang, Lianghao Xia, Yong Xu, Jian Pei

Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence.

Crime Prediction Decision Making +1

MetaSets: Meta-Learning on Point Sets for Generalizable Representations

no code implementations CVPR 2021 Chao Huang, Zhangjie Cao, Yunbo Wang, Jianmin Wang, Mingsheng Long

It is a challenging problem due to the substantial geometry shift from simulated to real data, such that most existing 3D models underperform due to overfitting the complete geometries in the source domain.

Domain Generalization Meta-Learning

Atomic Filter: a Weak Form of Shift Operator for Graph Signals

no code implementations1 Apr 2022 Lihua Yang, Qing Zhang, Qian Zhang, Chao Huang

In order to establish the theory of filtering, windowed Fourier transform and wavelet transform in the setting of graph signals, we need to extend the shift operation of classical signals to graph signals.

Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding

no code implementations26 Mar 2022 Zhilu Wang, Chao Huang, Qi Zhu

The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sensitive the model output is under input perturbations.

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

1 code implementation17 Feb 2022 Wei Wei, Chao Huang, Lianghao Xia, Yong Xu, Jiashu Zhao, Dawei Yin

In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users.

Contrastive Learning Meta-Learning

Joint Differentiable Optimization and Verification for Certified Reinforcement Learning

no code implementations28 Jan 2022 YiXuan Wang, Simon Zhan, Zhilu Wang, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu

In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties (e. g., safety, stability) under the learned controller.

Bilevel Optimization Model-based Reinforcement Learning +2

Collaborative Reflection-Augmented Autoencoder Network for Recommender Systems

1 code implementation10 Jan 2022 Lianghao Xia, Chao Huang, Yong Xu, Huance Xu, Xiang Li, WeiGuo Zhang

As the deep learning techniques have expanded to real-world recommendation tasks, many deep neural network based Collaborative Filtering (CF) models have been developed to project user-item interactions into latent feature space, based on various neural architectures, such as multi-layer perceptron, auto-encoder and graph neural networks.

Collaborative Filtering Recommendation Systems

Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling

1 code implementation7 Jan 2022 Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Mengyin Lu, Liefeng Bo

Due to the overlook of user's multi-behavioral patterns over different items, existing recommendation methods are insufficient to capture heterogeneous collaborative signals from user multi-behavior data.

Collaborative Filtering Recommendation Systems

Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning

1 code implementation IJCAI 2021 Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Liefeng Bo, Xiyue Zhang, Tianyi Chen

Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e. g., Theft, Robbery, Assault, Damage) which reveal fine-grained semantics of crimes.

Crime Prediction

Decision Making for Connected Automated Vehicles at Urban Intersections Considering Social and Individual Benefits

no code implementations5 Jan 2022 Peng Hang, Chao Huang, Zhongxu Hu, Chen Lv

To address the coordination issue of connected automated vehicles (CAVs) at urban scenarios, a game-theoretic decision-making framework is proposed that can advance social benefits, including the traffic system efficiency and safety, as well as the benefits of individual users.

Decision Making

Federated Natural Language Generation for Personalized Dialogue System

no code implementations13 Oct 2021 Yujie Lu, Chao Huang, Huanli Zhan, Yong Zhuang

FedNLG first pre-trains parameters of standard neural conversational model over a large dialogue corpus, and then fine-tune the model parameters and persona embeddings on specific datasets, in a federated manner.

Text Generation

Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks

1 code implementation8 Oct 2021 Huance Xu, Chao Huang, Yong Xu, Lianghao Xia, Hao Xing, Dawei Yin

Social recommendation which aims to leverage social connections among users to enhance the recommendation performance.

Recommendation Systems

Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

1 code implementation8 Oct 2021 Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Bo Zhang, Liefeng Bo

The overlook of multiplex behavior relations can hardly recognize the multi-modal contextual signals across different types of interactions, which limit the feasibility of current recommendation methods.

Recommendation Systems Vocal Bursts Type Prediction

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

1 code implementation8 Oct 2021 Xiyue Zhang, Chao Huang, Yong Xu, Lianghao Xia, Peng Dai, Liefeng Bo, Junbo Zhang, Yu Zheng

Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment.

Traffic Prediction

Social Recommendation with Self-Supervised Metagraph Informax Network

1 code implementation8 Oct 2021 Xiaoling Long, Chao Huang, Yong Xu, Huance Xu, Peng Dai, Lianghao Xia, Liefeng Bo

To model relation heterogeneity, we design a metapath-guided heterogeneous graph neural network to aggregate feature embeddings from different types of meta-relations across users and items, em-powering SMIN to maintain dedicated representations for multi-faceted user- and item-wise dependencies.

Collaborative Filtering Recommendation Systems

Graph Meta Network for Multi-Behavior Recommendation

1 code implementation8 Oct 2021 Lianghao Xia, Yong Xu, Chao Huang, Peng Dai, Liefeng Bo

Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions.

Meta-Learning Recommendation Systems +1

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

1 code implementation8 Oct 2021 Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, Yanfang Ye

While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.

Collaborative Filtering Recommendation Systems

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation

1 code implementation8 Oct 2021 Chao Huang, Jiahui Chen, Lianghao Xia, Yong Xu, Peng Dai, Yanqing Chen, Liefeng Bo, Jiashu Zhao, Jimmy Xiangji Huang

The learning process of intra- and inter-session transition dynamics are integrated, to preserve the underlying low- and high-level item relationships in a common latent space.

Multi-Task Learning Session-Based Recommendations

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

1 code implementation8 Oct 2021 Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, Liefeng Bo

In particular: i) complex inter-dependencies across different types of user behaviors; ii) the incorporation of knowledge-aware item relations into the multi-behavior recommendation framework; iii) dynamic characteristics of multi-typed user-item interactions.

Graph Attention Recommendation Systems

Recent Advances in Heterogeneous Relation Learning for Recommendation

no code implementations7 Oct 2021 Chao Huang

Recommender systems have played a critical role in many web applications to meet user's personalized interests and alleviate the information overload.

Recommendation Systems Relational Reasoning

Unidirectional substitutes and complements

no code implementations28 Aug 2021 Chao Huang

Under the framework of matching with continuous monetary transfers and quasi-linear utilities, we show that substitutes and complements are bidirectional for a pair of workers.

When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?

1 code implementation26 Aug 2021 Xu Liu, Yuxuan Liang, Chao Huang, Yu Zheng, Bryan Hooi, Roger Zimmermann

In view of this, one may ask: can we leverage the additional signals from contrastive learning to alleviate data scarcity, so as to benefit STG forecasting?

Contrastive Learning Data Augmentation +2

POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems

2 code implementations25 Jun 2021 Chao Huang, Jiameng Fan, Zhilu Wang, YiXuan Wang, Weichao Zhou, Jiajun Li, Xin Chen, Wenchao Li, Qi Zhu

We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).

Verification in the Loop: Correct-by-Construction Control Learning with Reach-avoid Guarantees

no code implementations6 Jun 2021 YiXuan Wang, Chao Huang, Zhaoran Wang, Zhilu Wang, Qi Zhu

Specifically, we leverage the verification results (computed reachable set of the system state) to construct feedback metrics for control learning, which measure how likely the current design of control parameters can meet the required reach-avoid property for safety and goal-reaching.

MetaSets:Meta-Learning on Point Sets for Generalizable Representations

no code implementations CVPR 2021 Chao Huang, Zhangjie Cao, Yunbo Wang, Jianmin Wang, Mingsheng Long

It is a challenging problem due to the substantial geometry shift from simulated to real data, such that most existing 3D models underperform due to overfitting the complete geometries in the source domain.

Domain Generalization

VSpSR: Explorable Super-Resolution via Variational Sparse Representation

no code implementations17 Apr 2021 Hangqi Zhou, Chao Huang, Shangqi Gao, Xiahai Zhuang

Super-resolution (SR) is an ill-posed problem, which means that infinitely many high-resolution (HR) images can be degraded to the same low-resolution (LR) image.


BERT-based Chinese Text Classification for Emergency Domain with a Novel Loss Function

no code implementations9 Apr 2021 Zhongju Wang, Long Wang, Chao Huang, Xiong Luo

This paper proposes an automatic Chinese text categorization method for solving the emergency event report classification problem.

Benchmarking General Classification +3

Decision Making of Connected Automated Vehicles at An Unsignalized Roundabout Considering Personalized Driving Behaviours

no code implementations14 Mar 2021 Peng Hang, Chao Huang, Zhongxu Hu, Yang Xing, Chen Lv

To improve the safety and efficiency of the intelligent transportation system, particularly in complex urban scenarios, in this paper a game theoretic decision-making framework is designed for connected automated vehicles (CAVs) at unsignalized roundabouts considering their personalized driving behaviours.

Decision Making motion prediction

Stable matching: an integer programming approach

no code implementations5 Mar 2021 Chao Huang

We provide a class of firms' preference profiles that satisfy this condition.

Partly Supervised Multitask Learning

no code implementations5 May 2020 Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang, Wei Fan, Yuan Xiao, Dingjun Hao, Zhen Qian, Demetri Terzopoulos

Leveraging self-supervision and adversarial training, we propose a novel general purpose semi-supervised, multiple-task model---namely, self-supervised, semi-supervised, multitask learning (S$^4$MTL)---for accomplishing two important tasks in medical imaging, segmentation and diagnostic classification.

Medical Image Segmentation

Analysis of Scoliosis From Spinal X-Ray Images

no code implementations15 Apr 2020 Abdullah-Al-Zubaer Imran, Chao Huang, Hui Tang, Wei Fan, Kenneth M. C. Cheung, Michael To, Zhen Qian, Demetri Terzopoulos

Leveraging a carefully-adjusted U-Net model with progressive side outputs, we propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement.

Non-Local Part-Aware Point Cloud Denoising

no code implementations14 Mar 2020 Chao Huang, Ruihui Li, Xianzhi Li, Chi-Wing Fu

This paper presents a novel non-local part-aware deep neural network to denoise point clouds by exploring the inherent non-local self-similarity in 3D objects and scenes.

Denoising Graph Attention +1

Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series

no code implementations10 Feb 2020 Xian Wu, Chao Huang, Pablo Roblesgranda, Nitesh Chawla

The prevalence of wearable sensors (e. g., smart wristband) is creating unprecedented opportunities to not only inform health and wellness states of individuals, but also assess and infer personal attributes, including demographic and personality attributes.

Representation Learning Time Series Analysis

Shape-Aware Organ Segmentation by Predicting Signed Distance Maps

no code implementations9 Dec 2019 Yuan Xue, Hui Tang, Zhi Qiao, Guanzhong Gong, Yong Yin, Zhen Qian, Chao Huang, Wei Fan, Xiaolei Huang

In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness.

Hippocampus Organ Segmentation

Few-Shot Knowledge Graph Completion

1 code implementation26 Nov 2019 Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla

Knowledge graphs (KGs) serve as useful resources for various natural language processing applications.

One-Shot Learning

Heterogeneous Deep Graph Infomax

1 code implementation19 Nov 2019 Yuxiang Ren, Bo Liu, Chao Huang, Peng Dai, Liefeng Bo, Jiawei Zhang

The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.

Classification General Classification +3

3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation

1 code implementation4 Sep 2019 Chao Huang, Hu Han, Qingsong Yao, Shankuan Zhu, S. Kevin Zhou

Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a single model with the addition of a minimal number of parameters steered to each task.

Image Classification Image Segmentation +3

ReachNN: Reachability Analysis of Neural-Network Controlled Systems

1 code implementation25 Jun 2019 Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, Qi Zhu

In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i. e., as long as they ensure that the neural networks are Lipschitz continuous.

Extreme Image Coding via Multiscale Autoencoders With Generative Adversarial Optimization

no code implementations8 Apr 2019 Chao Huang, Haojie Liu, Tong Chen, Qiu Shen, Zhan Ma

We propose a MultiScale AutoEncoder(MSAE) based extreme image compression framework to offer visually pleasing reconstruction at a very low bitrate.

Image Compression

Deep learning in bioinformatics: introduction, application, and perspective in big data era

1 code implementation28 Feb 2019 Yu Li, Chao Huang, Lizhong Ding, Zhongxiao Li, Yijie Pan, Xin Gao

Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics.

Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction

no code implementations3 Dec 2018 Hyunkwang Lee, Chao Huang, Sehyo Yune, Shahein H. Tajmir, Myeongchan Kim, Synho Do

Recent advancements in deep learning for automated image processing and classification have accelerated many new applications for medical image analysis.

BIG-bench Machine Learning Computed Tomography (CT) +2

SupportNet: solving catastrophic forgetting in class incremental learning with support data

1 code implementation8 Jun 2018 Yu Li, Zhongxiao Li, Lizhong Ding, Yijie Pan, Chao Huang, Yuhui Hu, Wei Chen, Xin Gao

A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic forgetting.

class-incremental learning Class Incremental Learning +1

Neural Tensor Factorization

no code implementations13 Feb 2018 Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh Chawla

Neural collaborative filtering (NCF) and recurrent recommender systems (RRN) have been successful in modeling user-item relational data.

Collaborative Filtering Link Prediction +1

Transfer Adversarial Hashing for Hamming Space Retrieval

no code implementations13 Dec 2017 Zhangjie Cao, Mingsheng Long, Chao Huang, Jian-Min Wang

Existing work on deep hashing assumes that the database in the target domain is identically distributed with the training set in the source domain.

Image Retrieval Retrieval

Cannot find the paper you are looking for? You can Submit a new open access paper.