Search Results for author: Lina Yao

Found 126 papers, 28 papers with code

Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement

no code implementations6 Apr 2024 Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao

Such a failure may overlook the conditionality between two domains and how it contributes to latent factor disentanglement, leading to negative transfer when domains are weakly correlated.

Disentanglement Transfer Learning

Learn When (not) to Trust Language Models: A Privacy-Centric Adaptive Model-Aware Approach

no code implementations4 Apr 2024 Chengkai Huang, Rui Wang, Kaige Xie, Tong Yu, Lina Yao

Despite their great success, the knowledge provided by the retrieval process is not always useful for improving the model prediction, since in some samples LLMs may already be quite knowledgeable and thus be able to answer the question correctly without retrieval.

Continual Learning Retrieval

CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models

1 code implementation28 Mar 2024 Saurav Jha, Dong Gong, Lina Yao

The deterministic nature of the existing finetuning methods makes them overlook the many possible interactions across the modalities and deems them unsafe for high-risk CL tasks requiring reliable uncertainty estimation.

Continual Learning

Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning

no code implementations27 Mar 2024 Huiyi Wang, Haodong Lu, Lina Yao, Dong Gong

We design each adapter module to consist of an adapter and a representation descriptor, specifically, implemented as an autoencoder.

Continual Learning

Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems

no code implementations26 Mar 2024 Siyu Wang, Xiaocong Chen, Lina Yao

Reinforcement Learning-based Recommender Systems (RLRS) have shown promise across a spectrum of applications, from e-commerce platforms to streaming services.

Computational Efficiency Decision Making +1

Uncertainty-aware Distributional Offline Reinforcement Learning

no code implementations26 Mar 2024 Xiaocong Chen, Siyu Wang, Tong Yu, Lina Yao

Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data.

Offline RL reinforcement-learning +1

Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning

no code implementations27 Feb 2024 Yun Li, Zhe Liu, Hang Chen, Lina Yao

Our framework evaluates the specificity of attributes by considering the diversity of objects they apply to and their related context.

Attribute Compositional Zero-Shot Learning +1

Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models

no code implementations23 Feb 2024 Yuzhe Zhang, YiPeng Zhang, Yidong Gan, Lina Yao, Chen Wang

We propose a novel method that utilizes the extensive knowledge contained within a large corpus of scientific literature to deduce causal relationships in general causal graph recovery tasks.

Causal Inference Retrieval

Foundation Models for Recommender Systems: A Survey and New Perspectives

no code implementations17 Feb 2024 Chengkai Huang, Tong Yu, Kaige Xie, Shuai Zhang, Lina Yao, Julian McAuley

Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs).

Recommendation Systems Representation Learning

Learning with Mixture of Prototypes for Out-of-Distribution Detection

1 code implementation5 Feb 2024 Haodong Lu, Dong Gong, Shuo Wang, Jason Xue, Lina Yao, Kristen Moore

To tackle these issues, we propose PrototypicAl Learning with a Mixture of prototypes (PALM) which models each class with multiple prototypes to capture the sample diversities, and learns more faithful and compact samples embeddings to enhance OOD detection.

Out-of-Distribution Detection Out of Distribution (OOD) Detection +1

HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization

no code implementations18 Jan 2024 Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang

Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features.

Contrastive Learning Domain Generalization

SCALA: Sparsification-based Contrastive Learning for Anomaly Detection on Attributed Networks

no code implementations3 Jan 2024 Enbo He, Yitong Hao, Yue Zhang, Guisheng Yin, Lina Yao

Besides, the node representation of normal entities can be perturbed easily by the noise relationships introduced by anomalous nodes.

Anomaly Detection Contrastive Learning

How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation

1 code implementation12 Dec 2023 Zhongyi Han, Guanglin Zhou, Rundong He, Jindong Wang, Tailin Wu, Yilong Yin, Salman Khan, Lina Yao, Tongliang Liu, Kun Zhang

We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation.

Anomaly Detection Autonomous Driving +6

Mask Propagation for Efficient Video Semantic Segmentation

1 code implementation NeurIPS 2023 Yuetian Weng, Mingfei Han, Haoyu He, Mingjie Li, Lina Yao, Xiaojun Chang, Bohan Zhuang

By reusing predictions from key frames, we circumvent the need to process a large volume of video frames individually with resource-intensive segmentors, alleviating temporal redundancy and significantly reducing computational costs.

Semantic Segmentation Video Semantic Segmentation

Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation

no code implementations4 Sep 2023 Jing Du, Zesheng Ye, Bin Guo, Zhiwen Yu, Lina Yao

Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains.

Preference Mapping

Reinforcement Learning for Generative AI: A Survey

no code implementations28 Aug 2023 Yuanjiang Cao, Quan Z. Sheng, Julian McAuley, Lina Yao

Deep Generative AI has been a long-standing essential topic in the machine learning community, which can impact a number of application areas like text generation and computer vision.

Inductive Bias Language Modelling +3

On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems

no code implementations22 Aug 2023 Xiaocong Chen, Siyu Wang, Julian McAuley, Dietmar Jannach, Lina Yao

Offline reinforcement learning empowers agents to glean insights from offline datasets and deploy learned policies in online settings.

Recommendation Systems reinforcement-learning

Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition

no code implementations22 Jul 2023 Yao Liu, Gangfeng Cui, Jiahui Luo, Lina Yao, Xiaojun Chang

Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions.

Action Recognition Temporal Action Localization

Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition

no code implementations5 May 2023 Jingcheng Li, Lina Yao, Binghao Li, Claude Sammut

Then the knowledge distillation method is applied to transfer the learned representation from the teacher model to a simpler DMFT student model, which consists of a lite version of the multi-modal spatial-temporal transformer module, to produce the results.

Feature Engineering Human Activity Recognition +1

Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation

no code implementations17 Apr 2023 Siyu Wang, Xiaocong Chen, Quan Z. Sheng, Yihong Zhang, Lina Yao

This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems.

Decision Making Disentanglement +1

Limits of Predictability in Top-N Recommendation

no code implementations23 Mar 2023 En Xu, Zhiwen Yu, Ying Zhang, Bin Guo, Lina Yao

This work investigates such predictability by studying the degree of regularity from a specific set of user behavior data.

Adversarially Contrastive Estimation of Conditional Neural Processes

no code implementations23 Mar 2023 Zesheng Ye, Jing Du, Lina Yao

Conditional Neural Processes~(CNPs) formulate distributions over functions and generate function observations with exact conditional likelihoods.

Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion

no code implementations15 Mar 2023 Yao Liu, Zesheng Ye, Binghao Li, Lina Yao

In this work, we propose to separately model these two factors by implicitly deriving a flexible distribution that describes complex pedestrians' movements, whereas incorporating predictive uncertainty of individuals with explicit density functions over their future locations.

Denoising Pedestrian Trajectory Prediction +1

Guided Image-to-Image Translation by Discriminator-Generator Communication

no code implementations7 Mar 2023 Yuanjiang Cao, Lina Yao, Le Pan, Quan Z. Sheng, Xiaojun Chang

The goal of Image-to-image (I2I) translation is to transfer an image from a source domain to a target domain, which has recently drawn increasing attention.

Generative Adversarial Network Image-to-Image Translation +1

Emerging Synergies in Causality and Deep Generative Models: A Survey

no code implementations29 Jan 2023 Guanglin Zhou, Shaoan Xie, GuangYuan Hao, Shiming Chen, Biwei Huang, Xiwei Xu, Chen Wang, Liming Zhu, Lina Yao, Kun Zhang

In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance.

Causal Identification Fairness +1

HTML: Hybrid Temporal-scale Multimodal Learning Framework for Referring Video Object Segmentation

no code implementations ICCV 2023 Mingfei Han, Yali Wang, Zhihui Li, Lina Yao, Xiaojun Chang, Yu Qiao

To tackle this problem, we propose a concise Hybrid Temporal-scale Multimodal Learning (HTML) framework, which can effectively align lingual and visual features to discover core object semantics in the video, by learning multimodal interaction hierarchically from different temporal scales.

Ranked #6 on Referring Video Object Segmentation on Refer-YouTube-VOS (using extra training data)

Object Referring Video Object Segmentation +2

Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning

no code implementations5 Nov 2022 Zhe Liu, Yun Li, Lina Yao, Xiaojun Chang, Wei Fang, XiaoJun Wu, Yi Yang

We design Semantic Attention (SA) and generative Knowledge Disentanglement (KD) to learn the dependence of feasibility and contextuality, respectively.

Compositional Zero-Shot Learning Disentanglement

Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation

no code implementations17 Sep 2022 Xiaocong Chen, Siyu Wang, Lina Yao, Lianyong Qi, Yong Li

It is more challenging to balance the exploration and exploitation in DRL RS where RS agent need to deeply explore the informative trajectories and exploit them efficiently in the context of recommender systems.

counterfactual Data Augmentation +3

The Neural Process Family: Survey, Applications and Perspectives

1 code implementation1 Sep 2022 Saurav Jha, Dong Gong, Xuesong Wang, Richard E. Turner, Lina Yao

We shed light on their potential to bring several recent advances in other deep learning domains under one umbrella.

Gaussian Processes Meta-Learning

Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey

no code implementations22 Aug 2022 Dalin Zhang, KaiXuan Chen, Yan Zhao, Bin Yang, Lina Yao, Christian S. Jensen

A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices.

Edge-computing Model Compression +1

Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems

no code implementations13 Aug 2022 Guanglin Zhou, Chengkai Huang, Xiaocong Chen, Xiwei Xu, Chen Wang, Liming Zhu, Lina Yao

Recognizing that confounders may be elusive, we propose a contrastive self-supervised learning to minimize exposure bias, employing inverse propensity scores and expanding the positive sample set.

Causal Inference counterfactual +2

Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects

no code implementations13 Aug 2022 Guanglin Zhou, Lina Yao, Xiwei Xu, Chen Wang, Liming Zhu

We regularly consider answering counterfactual questions in practice, such as "Would people with diabetes take a turn for the better had they choose another medication?".

counterfactual Counterfactual Inference +3

Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation

no code implementations10 Aug 2022 Siyu Wang, Xiaocong Chen, Lina Yao, Sally Cripps, Julian McAuley

Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems.

counterfactual Data Augmentation +3

IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation

no code implementations9 Aug 2022 Jing Du, Zesheng Ye, Lina Yao, Bin Guo, Zhiwen Yu

In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests.

Sequential Recommendation

See What You See: Self-supervised Cross-modal Retrieval of Visual Stimuli from Brain Activity

no code implementations7 Aug 2022 Zesheng Ye, Lina Yao, Yu Zhang, Sylvia Gustin

Recent studies demonstrate the use of a two-stage supervised framework to generate images that depict human perception to visual stimuli from EEG, referring to EEG-visual reconstruction.

Cross-Modal Retrieval EEG +1

A Survey on Participant Selection for Federated Learning in Mobile Networks

no code implementations8 Jul 2022 Behnaz Soltani, Venus Haghighi, Adnan Mahmood, Quan Z. Sheng, Lina Yao

The main challenges of FL is that end devices usually possess various computation and communication capabilities and their training data are not independent and identically distributed (non-IID).

Federated Learning Privacy Preserving

Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review

no code implementations2 Jul 2022 Hao Wang, Bin Guo, Yating Zeng, Yasan Ding, Chen Qiu, Ying Zhang, Lina Yao, Zhiwen Yu

The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence.

Unsupervised Knowledge Adaptation for Passenger Demand Forecasting

no code implementations8 Jun 2022 Can Li, Lei Bai, Wei Liu, Lina Yao, S Travis Waller

These multimodal forecasting models can improve accuracy but be less practical when different parts of multimodal datasets are owned by different institutions who cannot directly share data among them.

Towards Explanation for Unsupervised Graph-Level Representation Learning

1 code implementation20 May 2022 Qinghua Zheng, Jihong Wang, Minnan Luo, YaoLiang Yu, Jundong Li, Lina Yao, Xiaojun Chang

Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}"

Decision Making Graph Classification +2

Side-aware Meta-Learning for Cross-Dataset Listener Diagnosis with Subjective Tinnitus

no code implementations3 May 2022 Yun Li, Zhe Liu, Lina Yao, Molly Lucas, Jessica J. M. Monaghan, Yu Zhang

With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses.

BIG-bench Machine Learning EEG +1

Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis

no code implementations3 May 2022 Yun Li, Zhe Liu, Lina Yao, Jessica J. M. Monaghan, David Mcalpine

The side-aware unsupervised domain adaptation module adapts the class-irrelevant information as domain variance to a new dataset and excludes the variance to obtain the class-distill features for the new dataset classification.

EEG Unsupervised Domain Adaptation

Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation

no code implementations1 Apr 2022 Siyu Wang, Xiaocong Chen, Lina Yao

Recent advances have convinced that the ability of reinforcement learning to handle the dynamic process can be effectively applied in the interactive recommendation.

counterfactual reinforcement-learning +1

Contrastive Graph Learning for Population-based fMRI Classification

1 code implementation26 Mar 2022 Xuesong Wang, Lina Yao, Islem Rekik, Yu Zhang

Nonetheless, existing contrastive methods generate resemblant pairs only on pixel-level features of 3D medical images, while the functional connectivity that reveals critical cognitive information is under-explored.

Classification Graph Learning +1

Contrastive Conditional Neural Processes

no code implementations CVPR 2022 Zesheng Ye, Lina Yao

Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings.

Contrastive Learning Meta-Learning +2

Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning

no code implementations6 Jan 2022 Yun Li, Zhe Liu, Xiaojun Chang, Julian McAuley, Lina Yao

We further propose a differentiable dataset-level balance and update the weights in a linear annealing schedule to simulate network pruning and thus obtain the optimal structure for BSNet with dataset-level balance achieved.

Meta-Learning Network Pruning +1

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

Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning

no code implementations1 Dec 2021 Zhe Liu, Yun Li, Lina Yao, Julian McAuley, Sam Dixon

Our framework outperforms state-of-the-art algorithms on four benchmark datasets in both zero-shot and generalized zero-shot settings, which demonstrates the effectiveness of spiral learning in learning generalizable and complex correlations.

Attribute Zero-Shot Learning

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

Cycle-Balanced Representation Learning For Counterfactual Inference

1 code implementation29 Oct 2021 Guanglin Zhou, Lina Yao, Xiwei Xu, Chen Wang, Liming Zhu

With the widespread accumulation of observational data, researchers obtain a new direction to learn counterfactual effects in many domains (e. g., health care and computational advertising) without Randomized Controlled Trials(RCTs).

counterfactual Counterfactual Inference +2

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

AskMe: Joint Individual-level and Community-level Behavior Interaction for Question Recommendation

no code implementations11 Oct 2021 Nuo Li, Bin Guo, Yan Liu, Lina Yao, Jiaqi Liu, Zhiwen Yu

On the one hand, we model the rich correlations between the users' diverse behaviors (e. g., answer, follow, vote) to obtain the individual-level behavior interaction.

Community Question Answering

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

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 reinforcement-learning +2

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.

Attribute Classification +6

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

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.

Sequential Recommendation

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

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

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.

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.

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

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).

Collaborative Filtering Goal-Oriented Dialogue Systems +1

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

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) Segmentation

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.

Human Activity Recognition

Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction

no code implementations27 Nov 2019 Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo, Jing Jiang

Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision.

Entity Embeddings Relation +3

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 Generative Adversarial Network +1

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).

Bayesian Optimization Hyperparameter Optimization

Holographic Factorization Machines for Recommendation

1 code implementation AAAI 2019 Yi Tay, Shuai Zhang, Anh Tuan Luu, Siu Cheung Hui, Lina Yao, Tran Dang Quang Vinh

Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks.

Collaborative Filtering Retrieval

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.

Collaborative Filtering Inductive Bias +2

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

4 code implementations25 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.

Sequential Recommendation

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.

Brain Computer Interface

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 Embedding Knowledge Graph Embeddings +1

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

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.

Clustering Graph Clustering +2

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.

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

2 code implementations26 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 +1

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

no code implementations22 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 +1

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.

Clustering Feature Correlation +1

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