Search Results for author: Xianzhi Wang

Found 33 papers, 6 papers with code

GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation

no code implementations24 Nov 2023 Yakun Chen, Xianzhi Wang, Guandong Xu

The objective of spatiotemporal imputation is to estimate these missing values by understanding the inherent spatial and temporal relationships in the observed multivariate time series.

Graph Attention Imputation +4

Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems

no code implementations2 Dec 2021 Siyu Wang, Yuanjiang Cao, Xiaocong Chen, Lina Yao, Xianzhi Wang, Quan Z. Sheng

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

Adversarial Robustness counterfactual +3

An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning

no code implementations3 Nov 2021 Yun Li, Zhe Liu, Lina Yao, Xianzhi Wang, Julian McAuley, Xiaojun Chang

Zero-Shot Learning (ZSL) aims to transfer learned knowledge from observed classes to unseen classes via semantic correlations.

Generalized Zero-Shot Learning

Locality-Sensitive Experience Replay for Online Recommendation

no code implementations21 Oct 2021 Xiaocong Chen, Lina Yao, Xianzhi Wang, Julian McAuley

Existing studies encourage the agent to learn from past experience via experience replay (ER).

Recommendation Systems

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 reinforcement-learning +1

Global Convolutional Neural Processes

1 code implementation2 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

Multi-Center Federated Learning: Clients Clustering for Better Personalization

1 code implementation19 Aug 2021 Guodong Long, Ming Xie, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, Chengqi Zhang

By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i. e., centers) in FL.

Clustering Decision Making +1

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 reinforcement-learning +2

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).

Attribute Generalized Zero-Shot Learning +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 regression

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

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

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

Causality Learning: A New Perspective for Interpretable Machine Learning

no code implementations27 Jun 2020 Guandong Xu, Tri Dung Duong, Qian Li, Shaowu Liu, Xianzhi Wang

Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc.

BIG-bench Machine Learning Interpretable Machine Learning +2

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.

Position

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 reinforcement-learning +1

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.

Clustering

Multi-Center Federated Learning: Clients Clustering for Better Personalization

3 code implementations3 May 2020 Guodong Long, Ming Xie, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, Chengqi Zhang

However, due to the diverse nature of user behaviors, assigning users' gradients to different global models (i. e., centers) can better capture the heterogeneity of data distributions across users.

Clustering Federated 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-Aware Recommendation +3

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 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.

Brain Computer Interface

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.

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 +2

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