Search Results for author: Lina Yao

Found 63 papers, 17 papers with code

A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions

no code implementations8 Sep 2021 Xiaocong Chen, Lina Yao, Julian McAuley, Guanglin Zhou, Xianzhi Wang

In light of the emergence of deep reinforcement learning (DRL) in recommender systems research and several fruitful results in recent years, this survey aims to provide a timely and comprehensive overview of the recent trends of deep reinforcement learning in recommender systems.

Recommendation Systems

Global Convolutional Neural Processes

no code implementations2 Sep 2021 Xuesong Wang, Lina Yao, Xianzhi Wang, Hye-Young Paik, Sen Wang

Latent neural process, a member of NPF, is believed to be capable of modelling the uncertainty on certain points (local uncertainty) as well as the general function priors (global uncertainties).

Few-Shot Learning Gaussian Processes

Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions

no code implementations1 Sep 2021 Xiangtan Lin, Pengzhen Ren, Chung-Hsing Yeh, Lina Yao, Andy Song, Xiaojun Chang

Therefore, comprehensive surveys on this topic are essential to summarise challenges and solutions to foster future research.

Unsupervised Person Re-Identification

Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference

no code implementations3 May 2021 Xiaocong Chen, Lina Yao, Xianzhi Wang, Aixin Sun, Wenjie Zhang, Quan Z. Sheng

Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e. g., in reinforcement learning based recommender systems.

Recommendation Systems Scanpath prediction

Attribute-Modulated Generative Meta Learning for Zero-Shot Classification

no code implementations22 Apr 2021 Yun Li, Zhe Liu, Lina Yao, Xiaojun Chang

The promising strategies for ZSL are to synthesize visual features of unseen classes conditioned on semantic side information and to incorporate meta-learning to eliminate the model's inherent bias towards seen classes.

Classification General Classification +3

Task Aligned Generative Meta-learning for Zero-shot Learning

no code implementations3 Mar 2021 Zhe Liu, Yun Li, Lina Yao, Xianzhi Wang, Guodong Long

Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen).

Generalized Zero-Shot Learning Meta-Learning

Generative Adversarial U-Net for Domain-free Medical Image Augmentation

no code implementations12 Jan 2021 Xiaocong Chen, Yun Li, Lina Yao, Ehsan Adeli, Yu Zhang

The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing.

Computed Tomography (CT) Image Augmentation +1

Meta Gradient Boosting Neural Networks

no code implementations1 Jan 2021 Manqing Dong, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu

A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning.

Meta-Learning

Generative Inverse Deep Reinforcement Learning for Online Recommendation

no code implementations4 Nov 2020 Xiaocong Chen, Lina Yao, Aixin Sun, Xianzhi Wang, Xiwei Xu, Liming Zhu

Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process.

Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network

no code implementations12 Sep 2020 Can Li, Lei Bai, Wei Liu, Lina Yao, S Travis Waller

Accurate demand forecasting of different public transport modes(e. g., buses and light rails) is essential for public service operation. However, the development level of various modes often varies sig-nificantly, which makes it hard to predict the demand of the modeswith insufficient knowledge and sparse station distribution (i. e., station-sparse mode).

Multi-Task Learning

TRec: Sequential Recommender Based On Latent Item Trend Information

no code implementations11 Sep 2020 Ye Tao, Can Wang, Lina Yao, Weimin Li, Yonghong Yu

Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.

Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding

no code implementations14 Jul 2020 Zhe Liu, Xianzhi Wang, Lina Yao, Jake An, Lei Bai, Ee-Peng Lim

We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-$N$ purchase destinations of a consumer.

Spectrum-Guided Adversarial Disparity Learning

1 code implementation14 Jul 2020 Zhe Liu, Lina Yao, Lei Bai, Xianzhi Wang, Can Wang

It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class.

Activity Recognition Denoising

Recommender Systems for the Internet of Things: A Survey

no code implementations14 Jul 2020 May Altulyan, Lina Yao, Xianzhi Wang, Chaoran Huang, Salil S. Kanhere, Quan Z. Sheng

Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT).

Recommendation Systems

MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

1 code implementation7 Jul 2020 Manqing Dong, Feng Yuan, Lina Yao, Xiwei Xu, Liming Zhu

However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users.

Meta-Learning Recommendation Systems

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

3 code implementations NeurIPS 2020 Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang

We further propose an Adaptive Graph Convolutional Recurrent Network (AGCRN) to capture fine-grained spatial and temporal correlations in traffic series automatically based on the two modules and recurrent networks.

Graph Generation Multivariate Time Series Forecasting +4

NP-PROV: Neural Processes with Position-Relevant-Only Variances

no code implementations15 Jun 2020 Xuesong Wang, Lina Yao, Xianzhi Wang, Feiping Nie

Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations.

Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems

no code implementations14 Jun 2020 Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, Wei Emma Zhang

Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods.

Recommendation Systems

Agglomerative Neural Networks for Multi-view Clustering

no code implementations12 May 2020 Zhe Liu, Yun Li, Lina Yao, Xianzhi Wang, Feiping Nie

Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews.

Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems

no code implementations28 Apr 2020 Dai Hoang Tran, Quan Z. Sheng, Wei Emma Zhang, Salma Abdalla Hamad, Munazza Zaib, Nguyen H. Tran, Lina Yao, Nguyen Lu Dang Khoa

In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applications is the Conversational Recommender System (CRS).

Goal-Oriented Dialogue Systems Recommendation Systems

Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction

no code implementations18 Apr 2020 Zhe Liu, Lina Yao, Xianzhi Wang, Lei Bai, Jake An

Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models.

Representation Learning

Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation

no code implementations17 Apr 2020 Xiaocong Chen, Chaoran Huang, Lina Yao, Xianzhi Wang, Wei Liu, Wenjie Zhang

Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy.

Decision Making Knowledge Graphs

Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images

no code implementations12 Apr 2020 Xiaocong Chen, Lina Yao, Yu Zhang

The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy.

Computed Tomography (CT)

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective

no code implementations8 Apr 2020 Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu

A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.

Recommendation Systems

A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification

no code implementations23 Feb 2020 Weitao Xu, Xiang Zhang, Lina Yao, Wanli Xue, Bo Wei

In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN).

Classification Feature Selection +1

Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities

no code implementations21 Jan 2020 Kaixuan Chen, Dalin Zhang, Lina Yao, Bin Guo, Zhiwen Yu, Yunhao Liu

In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.

Activity Recognition

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

1 code implementation18 Sep 2019 Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu Zhang, Yong Li

Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.

EEG Feature Engineering +2

The Future of Misinformation Detection: New Perspectives and Trends

no code implementations9 Sep 2019 Bin Guo, Yasan Ding, Lina Yao, Yunji Liang, Zhiwen Yu

We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection.

Misinformation

Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals

2 code implementations31 Jul 2019 Xiang Zhang, Xiaocong Chen, Manqing Dong, Huan Liu, Chang Ge, Lina Yao

In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.

EEG Multi-Task Learning

Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning

1 code implementation31 Jul 2019 Xiang Zhang, Xiaocong Chen, Lina Yao, Chang Ge, Manqing Dong

Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e. g., computer version).

Hyperparameter Optimization

Quaternion Collaborative Filtering for Recommendation

no code implementations6 Jun 2019 Shuai Zhang, Lina Yao, Lucas Vinh Tran, Aston Zhang, Yi Tay

All in all, we conduct extensive experiments on six real-world datasets, demonstrating the effectiveness of Quaternion algebra in recommender systems.

Recommendation Systems Representation Learning

DeepRec: An Open-source Toolkit for Deep Learning based Recommendation

1 code implementation25 May 2019 Shuai Zhang, Yi Tay, Lina Yao, Bin Wu, Aixin Sun

In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow.

Recommendation Systems

Multi-agent Attentional Activity Recognition

no code implementations22 May 2019 Kaixuan Chen, Lina Yao, Dalin Zhang, Bin Guo, Zhiwen Yu

And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions.

Activity Recognition

A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

no code implementations10 May 2019 Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, Yu Zhang

Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices.

Adversarial Variational Embedding for Robust Semi-supervised Learning

1 code implementation7 May 2019 Xiang Zhang, Lina Yao, Feng Yuan

However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance.

General Classification

Quaternion Knowledge Graph Embeddings

1 code implementation NeurIPS 2019 Shuai Zhang, Yi Tay, Lina Yao, Qi Liu

In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge graph embeddings.

Knowledge Graph Completion Knowledge Graph Embedding +2

Learning to Recommend with Multiple Cascading Behaviors

no code implementations21 Sep 2018 Chen Gao, Xiangnan He, Dahua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Lina Yao, Yang song, Depeng Jin

To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task.

Multi-Task Learning Recommendation Systems

Next Item Recommendation with Self-Attention

no code implementations20 Aug 2018 Shuai Zhang, Yi Tay, Lina Yao, Aixin Sun

In this paper, we propose a novel sequence-aware recommendation model.

Metric Learning

Adversarial Collaborative Auto-encoder for Top-N Recommendation

no code implementations16 Aug 2018 Feng Yuan, Lina Yao, Boualem Benatallah

In this work, to address the above issue, we propose a general adversial training framework for neural network-based recommendation models, which improves both the model robustness and the overall performance.

GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest

no code implementations21 Jun 2018 Manqing Dong, Lina Yao, Xianzhi Wang, Boualem Benatallah, Shuai Zhang

We develop a gradient boost module and embed it into the proposed convolutional autoencoder with neural decision forest to improve the performance.

NeuRec: On Nonlinear Transformation for Personalized Ranking

no code implementations8 May 2018 Shuai Zhang, Lina Yao, Aixin Sun, Sen Wang, Guodong Long, Manqing Dong

Modeling user-item interaction patterns is an important task for personalized recommendations.

Recommendation Systems

Multi-modality Sensor Data Classification with Selective Attention

no code implementations16 Apr 2018 Xiang Zhang, Lina Yao, Chaoran Huang, Sen Wang, Mingkui Tan, Guodong Long, Can Wang

Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.

Classification General Classification

Metric Factorization: Recommendation beyond Matrix Factorization

2 code implementations13 Feb 2018 Shuai Zhang, Lina Yao, Yi Tay, Xiwei Xu, Xiang Zhang, Liming Zhu

In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations.

Adversarially Regularized Graph Autoencoder for Graph Embedding

4 code implementations13 Feb 2018 Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.

 Ranked #1 on Graph Clustering on Cora (F1 metric)

Graph Clustering Graph Embedding +1

Fullie and Wiselie: A Dual-Stream Recurrent Convolutional Attention Model for Activity Recognition

no code implementations21 Nov 2017 Kaixuan Chen, Lina Yao, Tao Gu, Zhiwen Yu, Xianzhi Wang, Dalin Zhang

Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR).

Activity Recognition

MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network

2 code implementations16 Nov 2017 Xiang Zhang, Lina Yao, Salil S. Kanhere, Yunhao Liu, Tao Gu, Kai-Xuan Chen

The proposed approach is evaluated over 3 datasets (two local and one public).

Human-Computer Interaction

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

1 code implementation26 Sep 2017 Xiang Zhang, Lina Yao, Quan Z. Sheng, Salil S. Kanhere, Tao Gu, Dalin Zhang

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots.

EEG General Classification

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

no code implementations26 Sep 2017 Xiang Zhang, Lina Yao, Dalin Zhang, Xianzhi Wang, Quan Z. Sheng, Tao Gu

In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition.

Activity Recognition EEG +1

Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface

1 code implementation22 Aug 2017 Dalin Zhang, Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen, Robert Boots

Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions.

Human-Computer Interaction Neurons and Cognition

Deep Learning based Recommender System: A Survey and New Perspectives

8 code implementations24 Jul 2017 Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay

This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems.

Information Retrieval Recommendation Systems

DeepKey: An EEG and Gait Based Dual-Authentication System

no code implementations6 Jun 2017 Xiang Zhang, Lina Yao, Chaoran Huang, Tao Gu, Zheng Yang, Yunhao Liu

Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics.

EEG Face Recognition +1

Uncovering Locally Discriminative Structure for Feature Analysis

no code implementations9 Jul 2016 Sen Wang, Feiping Nie, Xiaojun Chang, Xue Li, Quan Z. Sheng, Lina Yao

We propose a method that utilizes both the manifold structure of data and local discriminant information.

Unsupervised Feature Analysis with Class Margin Optimization

no code implementations3 Jun 2015 Sen Wang, Feiping Nie, Xiaojun Chang, Lina Yao, Xue Li, Quan Z. Sheng

In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features.

Feature Selection

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