Search Results for author: Xu Chen

Found 87 papers, 20 papers with code

SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection

no code implementations7 Oct 2021 Qin Liu, Han Deng, Chunfeng Lian, Xiaoyang Chen, Deqiang Xiao, Lei Ma, Xu Chen, Tianshu Kuang, Jaime Gateno, Pew-Thian Yap, James J. Xia

We propose a multi-stage coarse-to-fine CNN-based framework, called SkullEngine, for high-resolution segmentation and large-scale landmark detection through a collaborative, integrated, and scalable JSD model and three segmentation and landmark detection refinement models.

Semantic Segmentation

Shape-aware Multi-Person Pose Estimation from Multi-View Images

no code implementations ICCV 2021 Zijian Dong, Jie Song, Xu Chen, Chen Guo, Otmar Hilliges

In this paper we contribute a simple yet effective approach for estimating 3D poses of multiple people from multi-view images.

Multi-Person Pose Estimation

Top-N Recommendation with Counterfactual User Preference Simulation

no code implementations2 Sep 2021 Mengyue Yang, Quanyu Dai, Zhenhua Dong, Xu Chen, Xiuqiang He, Jun Wang

To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.

Causal Inference Recommendation Systems

Counterfactual Explainable Recommendation

no code implementations24 Aug 2021 Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, Yongfeng Zhang

Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.

Causal Inference Counterfactual Explanation +2

Neuron Campaign for Initialization Guided by Information Bottleneck Theory

1 code implementation14 Aug 2021 Haitao Mao, Xu Chen, Qiang Fu, Lun Du, Shi Han, Dongmei Zhang

Initialization plays a critical role in the training of deep neural networks (DNN).

Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective

no code implementations22 Jun 2021 Ning Zhang, Qian Ma, Xu Chen

Specifically, we model the long-term selfish participation behaviors of clients as an infinitely repeated game, with the stage game being a selfish participation game in one cross-silo FL process (SPFL).

Federated Learning

HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping

no code implementations18 Jun 2021 YuHan Wang, Xu Chen, Junwei Zhu, Wenqing Chu, Ying Tai, Chengjie Wang, Jilin Li, Yongjian Wu, Feiyue Huang, Rongrong Ji

In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results.

3D Face Reconstruction Face Recognition +1

A Game-Theoretic Approach to Multi-Agent Trust Region Optimization

1 code implementation12 Jun 2021 Ying Wen, Hui Chen, Yaodong Yang, Zheng Tian, Minne Li, Xu Chen, Jun Wang

Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration.

Atari Games Multi-agent Reinforcement Learning

TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning

1 code implementation11 Jun 2021 Xu Chen, Junshan Wang, Kunqing Xie

With the rapid growth of traffic sensors deployed, a massive amount of traffic flow data are collected, revealing the long-term evolution of traffic flows and the gradual expansion of traffic networks.

Continual Learning

TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data

no code implementations6 Jun 2021 Lun Du, Fei Gao, Xu Chen, Ran Jia, Junshan Wang, Jiang Zhang, Shi Han, Dongmei Zhang

To simultaneously extract spatial and relational information from tables, we propose a novel neural network architecture, TabularNet.

graph construction

Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective

no code implementations17 May 2021 Lun Du, Xu Chen, Fei Gao, Kunqing Xie, Shi Han, Dongmei Zhang

Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks.

Link Prediction Network Embedding +1

Contrastive Attraction and Contrastive Repulsion for Representation Learning

no code implementations8 May 2021 Huangjie Zheng, Xu Chen, Jiangchao Yao, Hongxia Yang, Chunyuan Li, Ya zhang, Hao Zhang, Ivor Tsang, Jingren Zhou, Mingyuan Zhou

Extensive large-scale experiments on standard vision tasks show that CACR not only consistently outperforms existing CL methods on benchmark datasets in representation learning, but also provides interpretable contrastive weights, demonstrating the efficacy of the proposed doubly contrastive strategy.

Contrastive Learning Representation Learning

SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes

1 code implementation ICCV 2021 Xu Chen, Yufeng Zheng, Michael J. Black, Otmar Hilliges, Andreas Geiger

However, this is problematic since the backward warp field is pose dependent and thus requires large amounts of data to learn.

Generating Multi-scale Maps from Remote Sensing Images via Series Generative Adversarial Networks

no code implementations31 Mar 2021 Xu Chen, Bangguo Yin, Songqiang Chen, Haifeng Li, Tian Xu

The series strategy avoids RS-m inconsistency as inputs are high-resolution large-scale RSIs, and reduces the distribution gap in multi-scale map generation through similar pixel distributions among multi-scale maps.

Image-to-Image Translation Translation

Large-scale simultaneous inference under dependence

no code implementations22 Feb 2021 Jinjin Tian, Xu Chen, Eugene Katsevich, Jelle Goeman, Aaditya Ramdas

Simultaneous, post-hoc inference is desirable in large-scale hypotheses testing as it allows for exploration of data while deciding on criteria for proclaiming discoveries.

Statistics Theory Methodology Statistics Theory

Causal Collaborative Filtering

1 code implementation3 Feb 2021 Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, Yongfeng Zhang

In this paper, we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommendation.

Collaborative Filtering Recommendation Systems

Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control

no code implementations21 Jan 2021 Qiong Wu, Xu Chen, Zhi Zhou, Liang Chen, Junshan Zhang

To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and capacity.

EC-SAGINs: Edge Computing-enhanced Space-Air-Ground Integrated Networks for Internet of Vehicles

no code implementations15 Jan 2021 Shuai Yu, Xiaowen Gong, Qian Shi, Xiaofei Wang, Xu Chen

After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas.

Decision Making Edge-computing +1

Discrete Knowledge Graph Embedding based on Discrete Optimization

no code implementations13 Jan 2021 Yunqi Li, Shuyuan Xu, Bo Liu, Zuohui Fu, Shuchang Liu, Xu Chen, Yongfeng Zhang

This paper proposes a discrete knowledge graph (KG) embedding (DKGE) method, which projects KG entities and relations into the Hamming space based on a computationally tractable discrete optimization algorithm, to solve the formidable storage and computation cost challenges in traditional continuous graph embedding methods.

Knowledge Graph Embedding

Generate Natural Language Explanations for Recommendation

no code implementations9 Jan 2021 Hanxiong Chen, Xu Chen, Shaoyun Shi, Yongfeng Zhang

Motivated by this problem, we propose to generate free-text natural language explanations for personalized recommendation.

Denoising Recommendation Systems

Multi-Agent Trust Region Learning

1 code implementation1 Jan 2021 Ying Wen, Hui Chen, Yaodong Yang, Zheng Tian, Minne Li, Xu Chen, Jun Wang

We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points.

Atari Games Multi-agent Reinforcement Learning +1

Causal World Models by Unsupervised Deconfounding of Physical Dynamics

no code implementations28 Dec 2020 Minne Li, Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jun Wang

The capability of imagining internally with a mental model of the world is vitally important for human cognition.

Dynamic driving and routing games for autonomous vehicles on networks: A mean field game approach

no code implementations15 Dec 2020 Kuang Huang, Xu Chen, Xuan Di, Qiang Du

In this paper, we aim to develop a game-theoretic model to solve for AVs's optimal driving strategies of velocity control in the interior of a road link and route choice at a junction node.

Autonomous Vehicles Decision Making Optimization and Control Systems and Control Systems and Control

FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring

no code implementations14 Dec 2020 Qiong Wu, Xu Chen, Zhi Zhou, Junshan Zhang

In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally.

Activity Recognition Personalized Federated Learning

CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices

no code implementations6 Dec 2020 Liekang Zeng, Xu Chen, Zhi Zhou, Lei Yang, Junshan Zhang

CoEdge utilizes available computation and communication resources at the edge and dynamically partitions the DNN inference workload adaptive to devices' computing capabilities and network conditions.

TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting

no code implementations30 Nov 2020 Xu Chen, Yuanxing Zhang, Lun Du, Zheng Fang, Yi Ren, Kaigui Bian, Kunqing Xie

Further analysis indicates that the locality and globality of the traffic networks are critical to traffic flow prediction and the proposed TSSRGCN model can adapt to the various temporal traffic patterns.

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

1 code implementation3 Nov 2020 Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, Ji-Rong Wen

In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.

Collaborative Filtering Recommendation Systems

GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs

no code implementations3 Nov 2020 Yunpeng Weng, Xu Chen, Liang Chen, Wei Liu

Most existing GNN models exploit a single type of aggregator (e. g., mean-pooling) to aggregate neighboring nodes information, and then add or concatenate the output of aggregator to the current representation vector of the center node.

Graph Attention Link Prediction +1

Learning on Attribute-Missing Graphs

2 code implementations3 Nov 2020 Xu Chen, Siheng Chen, Jiangchao Yao, Huangjie Zheng, Ya zhang, Ivor W Tsang

Thereby, designing a new GNN for these graphs is a burning issue to the graph learning community.

Graph Learning Link Prediction

Learning Euler's Elastica Model for Medical Image Segmentation

1 code implementation1 Nov 2020 Xu Chen, Xiangde Luo, Yitian Zhao, Shaoting Zhang, Guotai Wang, Yalin Zheng

Inspired by Euler's Elastica model and recent active contour models introduced into the field of deep learning, we propose a novel active contour with elastica (ACE) loss function incorporating Elastica (curvature and length) and region information as geometrically-natural constraints for the image segmentation tasks.

Medical Image Segmentation

CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation

1 code implementation29 Oct 2020 Yikun Xian, Zuohui Fu, Handong Zhao, Yingqiang Ge, Xu Chen, Qiaoying Huang, Shijie Geng, Zhou Qin, Gerard de Melo, S. Muthukrishnan, Yongfeng Zhang

User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user.

Knowledge Graphs Recommendation Systems

Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network

no code implementations25 Sep 2020 Shuqing Bian, Xu Chen, Wayne Xin Zhao, Kun Zhou, Yupeng Hou, Yang song, Tao Zhang, Ji-Rong Wen

Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data.

Text Matching

When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network

no code implementations22 Sep 2020 Shuai Yu, Xu Chen, Zhi Zhou, Xiaowen Gong, Di wu

Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilization of multiple 5G resources (e. g., computation, communication, storage and service resources); ii) low overhead offloading decision making and resource allocation strategies; and iii) privacy and security protection schemes.

Decision Making Edge-computing +1

Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation

1 code implementation15 Sep 2020 Xu Chen, Ya zhang, Ivor Tsang, Yuangang Pan, Jingchao Su

The majority of recent methods have explored shared-user representation to transfer knowledge across different domains.

Recommendation Systems

Decoupled Variational Embedding for Signed Directed Networks

1 code implementation28 Aug 2020 Xu Chen, Jiangchao Yao, Maosen Li, Ya zhang, Yan-Feng Wang

Comprehensive results on both link sign prediction and node recommendation task demonstrate the effectiveness of DVE.

Link Sign Prediction Node Classification +1

Learning Robust Node Representations on Graphs

no code implementations26 Aug 2020 Xu Chen, Ya zhang, Ivor Tsang, Yuangang Pan

Graph neural networks (GNN), as a popular methodology for node representation learning on graphs, currently mainly focus on preserving the smoothness and identifiability of node representations.

Contrastive Learning Representation Learning

Human Body Model Fitting by Learned Gradient Descent

no code implementations ECCV 2020 Jie Song, Xu Chen, Otmar Hilliges

We propose a novel algorithm for the fitting of 3D human shape to images.

Category Level Object Pose Estimation via Neural Analysis-by-Synthesis

no code implementations ECCV 2020 Xu Chen, Zijian Dong, Jie Song, Andreas Geiger, Otmar Hilliges

Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances.

Image Generation Pose Estimation

Collaborative Adversarial Learning for RelationalLearning on Multiple Bipartite Graphs

no code implementations16 Jul 2020 Jingchao Su, Xu Chen, Ya zhang, Siheng Chen, Dan Lv, Chenyang Li

The two-level alignment acts as two different constraints on different relations of the shared entities and facilitates better knowledge transfer for relational learning on multiple bipartite graphs.

Relational Reasoning Transfer Learning

Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence

no code implementations15 Jul 2020 Xin Tang, Xu Chen, Liekang Zeng, Shuai Yu, Lin Chen

With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN) based AI applications, which are generally resource-hungry and compute-intensive, such that an individual UE can hardly afford by itself in real time.


Learning Post-Hoc Causal Explanations for Recommendation

no code implementations30 Jun 2020 Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Xu Chen, Yongfeng Zhang

State-of-the-art recommender systems have the ability to generate high-quality recommendations, but usually cannot provide intuitive explanations to humans due to the usage of black-box prediction models.

Recommendation Systems

Network Massive MIMO Transmission Over Millimeter-Wave and Terahertz Bands: Mobility Enhancement and Blockage Mitigation

no code implementations6 May 2020 Li You, Xu Chen, Xiaohang Song, Fan Jiang, Wenjin Wang, Xiqi Gao, Gerhard Fettweis

Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation.

Knowledge Distillation for Mobile Edge Computation Offloading

no code implementations9 Apr 2020 Haowei Chen, Liekang Zeng, Shuai Yu, Xu Chen

In this article, we propose an edge computation offloading framework based on Deep Imitation Learning (DIL) and Knowledge Distillation (KD), which assists end devices to quickly make fine-grained decisions to optimize the delay of computation tasks online.

Imitation Learning Knowledge Distillation +1

XBlock-EOS: Extracting and Exploring Blockchain Data From EOSIO

no code implementations26 Mar 2020 Weilin Zheng, Zibin Zheng, Hong-Ning Dai, Xu Chen, PeiLin Zheng

It is challenging to process and analyze a high volume of raw EOSIO data and establish the mapping from original raw data to the well-grained datasets since it requires substantial efforts in extracting various types of data as well as sophisticated knowledge on software engineering and data analytics.

Computational Engineering, Finance, and Science Cryptography and Security

HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing

no code implementations22 Mar 2020 Deyin Liu, Xu Chen, Zhi Zhou, Qing Ling

We develop a novel \textit{hybrid parallelism} method, which is the key to HierTrain, to adaptively assign the DNN model layers and the data samples across the three levels of edge device, edge server and cloud center.

DeepCP: Deep Learning Driven Cascade Prediction Based Autonomous Content Placement in Closed Social Network

no code implementations9 Mar 2020 Qiong Wu, Muhong Wu, Xu Chen, Zhi Zhou, Kaiwen He, Liang Chen

Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users' QoE.

Decision Making

HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning

no code implementations26 Feb 2020 Siqi Luo, Xu Chen, Qiong Wu, Zhi Zhou, Shuai Yu

We further formulate a joint computation and communication resource allocation and edge association problem for device users under HFEL framework to achieve global cost minimization.

Distributed, Parallel, and Cluster Computing

Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework

no code implementations25 Feb 2020 Qiong Wu, Kaiwen He, Xu Chen

Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging.

Activity Recognition Edge-computing +1

Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach

no code implementations5 Nov 2019 Xuan Di, Xu Chen, Eric Talley

The game is then simulated with numerical examples to investigate the emergence of human drivers' moral hazard, the AV manufacturer's role in traffic safety, and the law maker's role in liability design.

Autonomous Driving

Cascading: Association Augmented Sequential Recommendation

no code implementations17 Oct 2019 Xu Chen, Kenan Cui, Ya zhang, Yan-Feng Wang

Recently, recommendation according to sequential user behaviors has shown promising results in many application scenarios.

Graph Embedding

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

no code implementations4 Oct 2019 En Li, Liekang Zeng, Zhi Zhou, Xu Chen

As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention.

Change Point Detection Edge-computing

Node Attribute Generation on Graphs

2 code implementations23 Jul 2019 Xu Chen, Siheng Chen, Huangjie Zheng, Jiangchao Yao, Kenan Cui, Ya zhang, Ivor W. Tsang

NANG learns a unifying latent representation which is shared by both node attributes and graph structures and can be translated to different modalities.

Data Augmentation General Classification +2

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

1 code implementation19 Jul 2019 Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network.

Edge-computing Face Recognition

A Survey on Neural Machine Reading Comprehension

no code implementations10 Jun 2019 Boyu Qiu, Xu Chen, Jungang Xu, Yingfei Sun

Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge.

Machine Reading Comprehension

Learning Active Contour Models for Medical Image Segmentation

no code implementations CVPR 2019 Xu Chen, Bryan M. Williams, Srinivasa R. Vallabhaneni, Gabriela Czanner, Rachel Williams, Yalin Zheng

Our aim was to tackle this limitation by developing a new model based on deep learning which takes into account the area inside as well as outside the region of interest as well as the size of boundaries during learning.

Medical Image Segmentation

Visually-aware Recommendation with Aesthetic Features

no code implementations2 May 2019 Wenhui Yu, Xiangnan He, Jian Pei, Xu Chen, Li Xiong, Jinfei Liu, Zheng Qin

While recent developments on visually-aware recommender systems have taken the product image into account, none of them has considered the aesthetic aspect.

Decision Making Recommendation Systems +1

Unpaired Pose Guided Human Image Generation

1 code implementation8 Jan 2019 Xu Chen, Jie Song, Otmar Hilliges

This paper studies the task of full generative modelling of realistic images of humans, guided only by coarse sketch of the pose, while providing control over the specific instance or type of outfit worn by the user.

Image-to-Image Translation Translation

Monocular Neural Image Based Rendering with Continuous View Control

2 code implementations ICCV 2019 Xu Chen, Jie Song, Otmar Hilliges

The approach is self-supervised and only requires 2D images and associated view transforms for training.

Novel View Synthesis

Ramp-based Twin Support Vector Clustering

no code implementations10 Dec 2018 Zhen Wang, Xu Chen, Chun-Na Li, Yuan-Hai Shao

Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost.

Robust Active Learning for Electrocardiographic Signal Classification

no code implementations21 Nov 2018 Xu Chen, Saratendu Sethi

The classification of electrocardiographic (ECG) signals is a challenging problem for healthcare industry.

Active Learning Classification +1

Variational Collaborative Learning for User Probabilistic Representation

no code implementations22 Sep 2018 Kenan Cui, Xu Chen, Jiangchao Yao, Ya zhang

Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users.

Collaborative Filtering Recommendation Systems

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

no code implementations19 Sep 2018 Xiaofei Wang, Yiwen Han, Chenyang Wang, Qiyang Zhao, Xu Chen, Min Chen

In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication.

Edge-computing Federated Learning

Aesthetic-based Clothing Recommendation

no code implementations16 Sep 2018 Wenhui Yu, Huidi Zhang, Xiangnan He, Xu Chen, Li Xiong, Zheng Qin

Considering that the aesthetic preference varies significantly from user to user and by time, we then propose a new tensor factorization model to incorporate the aesthetic features in a personalized manner.

Recommendation Systems

Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing

no code implementations14 Sep 2018 Tao Ouyang, Zhi Zhou, Xu Chen

To address this challenge in terms of the performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint.


Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy

no code implementations20 Jun 2018 En Li, Zhi Zhou, Xu Chen

As the backbone technology of machine learning, deep neural networks (DNNs) have have quickly ascended to the spotlight.

Topology Control for Energy-Efficient Localization in Mobile Underwater Sensor Networks using Stackelberg Game

no code implementations31 May 2018 Yali Yuan, Chencheng Liang, Megumi Kaneko, Xu Chen, Dieter Hogrefe

In this game, the sensor node acts as a leader taking into account factors such as 'two-hop' anchor nodes and energy consumption, while anchor nodes act as multiple followers, considering their ability to localize sensor nodes and their energy consumption.

Networking and Internet Architecture

Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

4 code implementations9 May 2018 Qingyao Ai, Vahid Azizi, Xu Chen, Yongfeng Zhang

Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items.

Collaborative Filtering Link Prediction +2

Explainable Recommendation: A Survey and New Perspectives

no code implementations30 Apr 2018 Yongfeng Zhang, Xu Chen

In this survey, we provide a comprehensive review for the explainable recommendation research.

Product Recommendation Recommendation Systems

Fictitious GAN: Training GANs with Historical Models

1 code implementation ECCV 2018 Hao Ge, Yin Xia, Xu Chen, Randall Berry, Ying Wu

Inspired by the fictitious play learning process, a novel training method, referred to as Fictitious GAN, is introduced.

Superposition-Assisted Stochastic Optimization for Hawkes Processes

no code implementations13 Feb 2018 Hongteng Xu, Xu Chen, Lawrence Carin

We consider the learning of multi-agent Hawkes processes, a model containing multiple Hawkes processes with shared endogenous impact functions and different exogenous intensities.

Recommendation Systems Stochastic Optimization

Training Generative Adversarial Networks via Primal-Dual Subgradient Methods: A Lagrangian Perspective on GAN

no code implementations ICLR 2018 Xu Chen, Jiang Wang, Hao Ge

This formulation shows the connection between the standard GAN training process and the primal-dual subgradient methods for convex optimization.

Visually Explainable Recommendation

no code implementations31 Jan 2018 Xu Chen, Yongfeng Zhang, Hongteng Xu, Yixin Cao, Zheng Qin, Hongyuan Zha

By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner.

Recommendation Systems

Texture Object Segmentation Based on Affine Invariant Texture Detection

no code implementations23 Dec 2017 Jianwei Zhang, Xu Chen, Xuezhong Xiao

To solve the issue of segmenting rich texture images, a novel detection methods based on the affine invariable principle is proposed.

Edge Detection Semantic Segmentation

Benefits from Superposed Hawkes Processes

no code implementations14 Oct 2017 Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin

The superposition of Hawkes processes is demonstrated to be beneficial for tightening the upper bound of excess risk under certain conditions, and we show the feasibility of the benefit in typical situations.

Point Processes Recommendation Systems

Feedback-Controlled Sequential Lasso Screening

no code implementations21 Aug 2016 Yun Wang, Xu Chen, Peter J. Ramadge

In this context, we propose and explore a feedback controlled sequential screening scheme.

Model Selection

Multi-lingual Geoparsing based on Machine Translation

no code implementations6 Nov 2015 Xu Chen, Han Zhang, Judith Gelernter

Our results for geoparsing Chinese and Arabic text using our multi-lingual geoparsing method are comparable to our results for geoparsing English text with our English tools.

Machine Translation Translation

Deep Haar Scattering Networks

no code implementations30 Sep 2015 Xiuyuan Cheng, Xu Chen, Stephane Mallat

An orthogonal Haar scattering transform is a deep network, computed with a hierarchy of additions, subtractions and absolute values, over pairs of coefficients.

Classification General Classification

Unsupervised Deep Haar Scattering on Graphs

no code implementations NeurIPS 2014 Xu Chen, Xiuyuan Cheng, Stéphane Mallat

The classification of high-dimensional data defined on graphs is particularly difficult when the graph geometry is unknown.

Classification Dimensionality Reduction +1

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