no code implementations • 30 May 2024 • Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu
While Positive-Unlabeled (PU) learning is vital in many real-world scenarios, its application to graph data still remains under-explored.
1 code implementation • 20 May 2024 • Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Salman Khan, Xin Gao, Lina Yao
We find that the success of ICL heavily relies on the choice of demonstration, mirroring challenges seen in large language models but introducing unique complexities for LMMs facing distribution shifts.
no code implementations • 12 May 2024 • Yao Liu, Quan Z. Sheng, Lina Yao
In response, we propose the Energy Plan Denoising (EPD) model for stochastic trajectory prediction.
no code implementations • 11 May 2024 • Yao Liu, Ruoyu Wang, Yuanjiang Cao, Quan Z. Sheng, Lina Yao
The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation.
no code implementations • 6 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.
no code implementations • 4 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.
1 code implementation • 28 Mar 2024 • Saurav Jha, Dong Gong, Lina Yao
However, the domain mismatch between the pre-training and the downstream CL tasks calls for finetuning of the CLIP on the latter.
no code implementations • 27 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.
no code implementations • 26 Mar 2024 • Xiaocong Chen, Siyu Wang, Tong Yu, Lina Yao
Offline reinforcement learning (RL) presents distinct challenges as it relies solely on observational data.
no code implementations • 26 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.
no code implementations • 27 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.
no code implementations • 23 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.
no code implementations • 21 Feb 2024 • Hongtao Huang, Xiaojun Chang, Wen Hu, Lina Yao
In this paper, we propose MatchNAS, a novel scheme for porting DNNs to mobile devices.
no code implementations • 17 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).
no code implementations • 16 Feb 2024 • Xiangyu Zhang, Daijiao Liu, Hexin Liu, Qiquan Zhang, Hanyu Meng, Leibny Paola Garcia, Eng Siong Chng, Lina Yao
Recently, Denoising Diffusion Probabilistic Models (DDPMs) have attained leading performances across a diverse range of generative tasks.
no code implementations • 14 Feb 2024 • Shiyi Yang, Lina Yao, Chen Wang, Xiwei Xu, Liming Zhu
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks.
1 code implementation • 5 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
no code implementations • 18 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.
no code implementations • 3 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.
no code implementations • 26 Dec 2023 • Yao Liu, Binghao Li, Xianzhi Wang, Claude Sammut, Lina Yao
We propose Attention-aware Social Graph Transformer Networks for multi-modal trajectory prediction.
1 code implementation • 12 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.
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.
no code implementations • 18 Sep 2023 • Yang Zhang, YuFei Wang, Kai Wang, Quan Z. Sheng, Lina Yao, Adnan Mahmood, Wei Emma Zhang, Rongying Zhao
Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs.
no code implementations • 4 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.
no code implementations • 28 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.
no code implementations • 22 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.
no code implementations • 22 Jul 2023 • Yao Liu, Gangfeng Cui, Jiahui Luo, Xiaojun Chang, Lina Yao
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.
no code implementations • 5 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.
no code implementations • 17 Apr 2023 • Siyu Wang, Xiaocong Chen, Dietmar Jannach, Lina Yao
Reinforcement learning-based recommender systems have recently gained popularity.
no code implementations • 17 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.
no code implementations • 23 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.
no code implementations • 23 Mar 2023 • Zesheng Ye, Jing Du, Lina Yao
Conditional Neural Processes~(CNPs) formulate distributions over functions and generate function observations with exact conditional likelihoods.
no code implementations • 15 Mar 2023 • Yao Liu, Zesheng Ye, Rui Wang, Binghao Li, Quan Z. Sheng, Lina Yao
Tremendous efforts have been put forth on predicting pedestrian trajectory with generative models to accommodate uncertainty and multi-modality in human behaviors.
no code implementations • 7 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
no code implementations • ICCV 2023 • Yun Li, Zhe Liu, Saurav Jha, Sally Cripps, Lina Yao
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects.
no code implementations • 7 Feb 2023 • Dawen Zhang, Shidong Pan, Thong Hoang, Zhenchang Xing, Mark Staples, Xiwei Xu, Lina Yao, Qinghua Lu, Liming Zhu
The right to be forgotten (RTBF) is motivated by the desire of people not to be perpetually disadvantaged by their past deeds.
no code implementations • 29 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.
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)
no code implementations • 5 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.
no code implementations • 26 Oct 2022 • Can Li, Lei Bai, Lina Yao, S. Travis Waller, Wei Liu
Transportation is the backbone of the economy and urban development.
no code implementations • 17 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.
1 code implementation • 1 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.
no code implementations • 22 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.
no code implementations • 13 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?".
no code implementations • 13 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.
no code implementations • 10 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.
no code implementations • 9 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.
no code implementations • 7 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.
2 code implementations • 16 Jul 2022 • Mingjie Li, Rui Liu, Guangsi Shi, Mingfei Han, Changling Li, Lina Yao, Xiaojun Chang, Ling Chen
To further enhance forecasting accuracy, we introduce a memory-driven decoder.
no code implementations • 8 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).
no code implementations • 2 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.
no code implementations • 8 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.
1 code implementation • 20 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?}"
no code implementations • 3 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.
no code implementations • 3 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.
no code implementations • CVPR 2022 • Mingfei Han, David Junhao Zhang, Yali Wang, Rui Yan, Lina Yao, Xiaojun Chang, Yu Qiao
Learning spatial-temporal relation among multiple actors is crucial for group activity recognition.
no code implementations • 1 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.
1 code implementation • 26 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.
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.
no code implementations • 6 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.
no code implementations • 2 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.
no code implementations • 1 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.
no code implementations • 3 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.
1 code implementation • 29 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).
no code implementations • 21 Oct 2021 • Xiaocong Chen, Lina Yao, Xianzhi Wang, Julian McAuley
Existing studies encourage the agent to learn from past experience via experience replay (ER).
no code implementations • 11 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.
no code implementations • 8 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.
1 code implementation • 2 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).
no code implementations • 1 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.
no code implementations • CVPR 2021 • Zhihui Li, Lina Yao
Temporal action detection on unconstrained videos has seen significant research progress in recent years.
no code implementations • 3 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.
no code implementations • 22 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.
no code implementations • 3 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).
no code implementations • 12 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.
no code implementations • 1 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.
no code implementations • 4 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.
no code implementations • 12 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).
no code implementations • 11 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.
no code implementations • 14 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.
1 code implementation • 14 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.
no code implementations • 14 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).
1 code implementation • 7 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.
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.
Ranked #2 on Traffic Prediction on BJTaxi
no code implementations • 16 Jun 2020 • Xiaocong Chen, Lina Yao, Tao Zhou, Jinming Dong, Yu Zhang
Diagnosis from chest CT images is a promising direction.
no code implementations • 15 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.
no code implementations • 14 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.
no code implementations • 12 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.
no code implementations • 28 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).
no code implementations • 20 Apr 2020 • Chaoyi Zhang, Yang song, Lina Yao, Weidong Cai
Point cloud is a principal data structure adopted for 3D geometric information encoding.
no code implementations • 18 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.
no code implementations • 17 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.
no code implementations • 12 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.
no code implementations • 8 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.
no code implementations • 23 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).
no code implementations • 21 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.
no code implementations • 27 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.
1 code implementation • 18 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.
no code implementations • 9 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.
1 code implementation • 31 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).
2 code implementations • 31 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.
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.
no code implementations • 6 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.
1 code implementation • 26 May 2019 • Feng Yuan, Lina Yao, Boualem Benatallah
Cross-domain recommendation has long been one of the major topics in recommender systems.
4 code implementations • 25 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.
1 code implementation • 24 May 2019 • Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, Quan Z. Sheng
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services.
no code implementations • 22 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.
no code implementations • 10 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.
2 code implementations • 10 May 2019 • Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang
The resulting graph of prototypes can be continually re-used and updated for new tasks and classes.
1 code implementation • 7 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.
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.
Ranked #4 on Link Prediction on FB15k
no code implementations • 12 Nov 2018 • Kaixuan Chen, Lina Yao, Dalin Zhang, Xiaojun Chang, Guodong Long, Sen Wang
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing.
no code implementations • 21 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.
no code implementations • 20 Aug 2018 • Shuai Zhang, Yi Tay, Lina Yao, Aixin Sun
In this paper, we propose a novel sequence-aware recommendation model.
no code implementations • 16 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.
no code implementations • 21 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.
no code implementations • 17 May 2018 • Kaixuan Chen, Lina Yao, Xianzhi Wang, Dalin Zhang, Tao Gu, Zhiwen Yu, Zheng Yang
Multimodal features play a key role in wearable sensor-based human activity recognition (HAR).
no code implementations • 9 May 2018 • Manqing Dong, Lina Yao, Xianzhi Wang, Boualem Benatallah, Chaoran Huang, Xiaodong Ning
Online reviews play an important role in influencing buyers' daily purchase decisions.
no code implementations • 8 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.
no code implementations • 16 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.
4 code implementations • 13 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 #5 on Link Prediction on Pubmed
2 code implementations • 13 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.
no code implementations • 21 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).
2 code implementations • 16 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
2 code implementations • 26 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.
no code implementations • 26 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.
no code implementations • 22 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
8 code implementations • 24 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.
no code implementations • 6 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.
no code implementations • 9 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.
no code implementations • 3 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.