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Wei Jin

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24 papers

2 author rows

EAAI Journal 2026 Journal Article

Global frequency-aware multi-scale feature learning for point cloud normal estimation

Wei Jin
Jun Zhou
Nannan Li
Xiuping Liu

Estimating accurate surface normals from point clouds remains a core challenge in three-dimensional (3D) computer vision due to irregular sampling and the difficulty of modeling global geometric context. In this paper, we propose a frequency-domain learning framework that addresses these issues by preserving global information throughout the feature extraction process. Specifically, we introduce a Fourier-Based Multi-Branch Patch Refinement Module at the data level to enhance patch representation with spectral cues, and a Fourier-Based Feature Refinement Layer to integrate local and global geometric features. A multi-scale fusion strategy is further adopted to ensure hierarchical consistency across resolutions. Compared to existing spatial-domain strategies, our method improves global context awareness by incorporating frequency-domain information, effectively mitigating the loss of global features commonly introduced during early-stage local convolutional operations. Experimental results demonstrate consistent performance improvements over prior methods, with gains of 1. 0% on the Point Cloud Property Network (PCPNet) dataset, which is a benchmark for learning local 3D shape properties from raw point clouds, 0. 76% on the Famous Shape dataset (FamousShape), which consists of several well-known 3D mesh models such as the Utah Teapot and Stanford Bunny, and 0. 65% on the Scene Meshes dataset with Annotations (SceneNN), which is a richly annotated collection of indoor 3D scenes.

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JBHI Journal 2026 Journal Article

HP-DIL: Deep heterogeneity profiling with graph-informed disentangled interaction learning for MRI-based liver fibrosis staging

Nan Wu
Yutao Wang
Jinhao Huo
Jian Zhang
Qianjiang Ding
Wei Jin

Liver fibrosis staging (LFS) informs treatment decisions and prognostic assessment in liver disease. Multiparametric MRI enables non-invasive, quantitative characterization of fibrosis-related tissue changes across the whole liver. Although deep-learning-based MRI analysis has advanced automated LFS, two bottlenecks remain: (i) etiology- and tissue-level heterogeneities reduce feature consistency across patients and liver regions; (ii) the lack of explicit modeling of inter-regional and inter-biomarker interactions biases models toward isolated imaging cues, leading to spurious correlations and limited generalizability. Here, we introduce a deep heterogeneity profiling framework with graph-informed disentangled interaction learning (HP-DIL) to enable accurate and interpretable LFS. HP-DIL first performs a biologically inspired, unsupervised subregion discovery stage, which fuses multiparametric MRI signals, spatial-texture coherence, and anatomical priors to construct subject-level graphs for heterogeneity profiling while preserving hepatic morphology. Within each subject, identified subregions are encoded as graph nodes carrying spatial coordinates, geometry, and multiparametric MRI attributes, forming a spatial-semantic interaction graph. A global-local graph transformer subsequently captures higher-order interactions among node-level representations within the constructed graph. Based on causal inference principles, we introduce a disentangled interaction mechanism (DIM) that decouples representative node-level features from whole-graph embeddings. An information-theoretic optimization is adopted to preserve disease-relevant signals while mitigating spurious correlations. Experiments on two external test cohorts from three external multi-vendor centers demonstrate that HP-DIL achieves competitive accuracy and cross-center generalizability. Moreover, we clarify the imaging relevance of the subregions identified by HP-DIL, with qualitative analysis showing close agreement between DIM-highlighted regions and radiological assessment. These findings support HP-DIL's potential for reliable clinical deployment in non-invasive LFS.

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AAAI Conference 2025 Conference Paper

Analytical-Chemistry-Informed Transformer for Infrared Spectra Modeling

Shiluo Huang
Yining Jin
Wei Jin
Ying Mu

Infrared (IR) spectroscopy is a fundamental technique in analytical chemistry. Recently, deep learning (DL) has drawn great interest as the modeling method of infrared spectral data. However, unlike vision or language tasks, IR spectral data modeling is faced with the problem of calibration transfer and has distinctive characteristics. Introducing the prior knowledge of IR spectroscopy could guide the DL methods to learn representations aligned with the domain-invariant characteristics of spectra, and thus improve the performance. Despite such potential, there is a notable absence of DL methods that incorporate such inductive bias. To this end, we propose Analytical-Chemistry-Informed Transformer (ACT) with two modules informed by the field knowledge in analytical chemistry. First, ACT includes learnable spectral processing inspired by chemometrics, which comprises spectral pre-processing, tokenization, and post-processing. Second, a straightforward yet effective representation learning mechanism, namely spectral-attention, is incorporated into ACT. Spectral-attention utilizes the intra-spectral and inter-spectral correlations to extract spectral representations. Empirical results show that ACT has achieved competitive results in 9 analytical tasks covering applications across pharmacy, chemistry, and agriculture. Compared with existing networks, ACT reduces the root mean square error of prediction (RMSEP) by more than 20% in calibration transfer tasks. These results indicate that DL methods in IR spectroscopy could benefit from the integration of prior knowledge in analytical chemistry.

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EAAI Journal 2025 Journal Article

Causality thinking for large-scale long-tailed video action recognition

Zhengjin Zhang
Nannan Li
Wenmin Wang
Huiwen Guo
Wei Jin
Sudan Huang

Video action recognition aims to accurately classify actions in video data by leveraging spatial–temporal representations. However, this task faces two major challenges: (1) the intrinsic long-tailed data distribution in real-world scenarios, which skews model learning toward overrepresented classes, and (2) the presence of spurious correlations in the data, which undermines prediction reliability. In this paper, we propose a causal inference framework for long-tailed video action recognition. A novel debiasing approach with Causal Intervention and Counterfactual Reasoning (CICR) is proposed to yield more robust predictions. The Structural Causal Model (SCM) is constructed to identify and analyze the causal relationships among spatial–temporal variables and long-tailed class distributions. Specifically, we first diagnose momentum as the confounder and use the intervention to eliminate the spurious relationship existed among video frame appearance features. The counterfactual reasoning is then utilized during inference to calculate the direct causal effect more accurately. During experiments conducted on two large-scale datasets, the proposed approach improved video action recognition accuracy by 1. 9% on EPIC-KITCHENS-100 and 3. 65% on Something-Something-V2-LT, thereby showing the effectiveness of the CICR model in mitigating spurious correlation and long-tailed distribution issues. Codes are available at https: //github. com/sandyzhang2021/Causality-for-long-tailed-video-action-recognition.

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JMLR Journal 2025 Journal Article

Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables

Wei Jin
Yang Ni
Amanda B. Spence
Leah H. Rubin
Yanxun Xu

We consider the problem of causal discovery from longitudinal observational data. We develop a novel framework that simultaneously discovers the time-lagged causality and the possibly cyclic instantaneous causality. Under common causal discovery assumptions, combined with additional instrumental information typically available in longitudinal data, we prove the proposed model is generally identifiable. To the best of our knowledge, this is the first causal identification theory for directed graphs with general cyclic patterns that achieves unique causal identifiability. Structural learning is carried out in a fully Bayesian fashion. Through extensive simulations and an application to the Women's Interagency HIV Study, we demonstrate the identifiability, utility, and superiority of the proposed model against state-of-the-art alternative methods. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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NeurIPS Conference 2025 Conference Paper

GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights

Shengbo Gong
Juntong Ni
Noveen Sachdeva
Carl Yang
Wei Jin

Graph condensation (GC) is an emerging technique designed to learn a significantly smaller graph that retains the essential information of the original graph. This condensed graph has shown promise in accelerating graph neural networks while preserving performance comparable to those achieved with the original, larger graphs. Additionally, this technique facilitates downstream applications like neural architecture search and deepens our understanding of redundancies in large graphs. Despite the rapid development of GC methods, particularly for node classification, a unified evaluation framework is still lacking to systematically compare different GC methods or clarify key design choices for improving their effectiveness. To bridge these gaps, we introduce GC4NC, a comprehensive framework for evaluating diverse GC methods on node classification across multiple dimensions including performance, efficiency, privacy preservation, denoising ability, NAS effectiveness, and transferability. Our systematic evaluation offers novel insights into how condensed graphs behave and the critical design choices that drive their success. These findings pave the way for future advancements in GC methods, enhancing both performance and expanding their real-world applications. The code is available at https: //github. com/Emory-Melody/GraphSlim/tree/main/benchmark.

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EAAI Journal 2025 Journal Article

Harnessing prototype networks for novel plant species and disease classification in open-world scenarios

Jiuqing Dong
Wei Jin
Alvaro Fuentes
Jaehwan Lee
Yongchae Jeong
Sook Yoon
Dong Sun Park

Identifying novel plant categories and diseases in open-world scenarios is crucial for modern agricultural production and applications. Recent studies have extended plant disease recognition from closed-set to open-set scenarios, aiming to reject samples that do not belong to known classes. However, beyond rejection, it is necessary to classify unknown samples rather than merely labeling them as ”unknown. ” This paper assumes that images of unknown samples, rejected by open-set recognition algorithms, are available. We aim to classify these unlabeled samples by leveraging prior knowledge from a labeled set. To the best of our knowledge, no existing research has addressed the classification of unknown plant species and diseases. To fill this gap, we propose a novel prototype network that models the category space relationship between known and unknown classes. Specifically, we learn a prototype vector for each known category, enabling samples to obtain distance-based category probabilities by measuring their similarity to these prototypes. This approach captures complex class boundaries more effectively than linear classification models, offering greater flexibility and accuracy. Additionally, we employ a knowledge distillation loss to optimize the category space relationship and calculate a consistency loss to balance the model’s classification performance for both known and unknown classes. To further boost performance, we incorporate the pre-trained model dino-v2. Experiments on the large-scale plant specimen dataset Herbarium19 and the plant disease dataset Plant Village demonstrate that our method surpasses baseline approaches, improving novel class accuracy by 1%–30%. This research contributes to intelligent agriculture, and we will release the code to facilitate future work in the field.

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YNIMG Journal 2025 Journal Article

Transcranial vibration stimulation at 40 Hz induced neural activity and promoted the coupling of global brain activity and cerebrospinal fluid flow

Linghan Kong
Wei Jin
Yue Jiang
Fuhua Yan
Jun Liu
Eric C. Leuthardt
Guang-Zhong Yang
Yuan Feng

BACKGROUND: Neuroscience advances have highlighted the potential of non-invasive brain stimulation in influencing cognitive and emotional processes. Conventional stimulation methods such as electrical, magnetic, and ultrasound have been studied intensively, but little is known about the mechanical stimulation. OBJECTIVE: To investigate the effects of 40 Hz transcranial vibration stimulation (TVS) on human brain activity, specifically focusing on changes in the Amplitude of Low-Frequency Fluctuation (ALFF), fractional ALFF (fALFF) and Regional Homogeneity (ReHo) as measures of spontaneous brain activity. Additionally, this study investigates alterations in the global blood-oxygen-level-dependent (gBOLD) signal and cerebrospinal fluid (CSF) inflow coupling, which serve as indicators of glymphatic system function. METHODS: A custom-built head actuator was used to apply 40 Hz TVS to human brain. Functional magnetic resonance imaging (fMRI) were performed before and after 5 mins TVS to explore the changes in ALFF and fALFF and the coupling of global brain activity with cerebrospinal fluid flow (CSF), which is related to the glymphatic clearance. RESULTS: Significant increases were observed in both ALFF and fALFF metrics, indicating that 40 Hz TVS effectively enhanced spontaneous brain activity. Additionally, 40 Hz TVS promoted the synchronization of overall brain activity with CSF, suggesting an improvement in glymphatic clearance processes, an effect that 30 Hz or 50 Hz TVS did not replicate. CONCLUSION: Non-invasive brain stimulation using TVS provided important implications for modulating brain physiology and showed prospective therapeutic benefits for neurological diseases.

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IJCAI Conference 2024 Conference Paper

A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation

Mohammad Hashemi
Shengbo Gong
Juntong Ni
Wenqi Fan
B. Aditya Prakash
Wei Jin

Many real-world datasets can be naturally represented as graphs, spanning a wide range of domains. However, the increasing complexity and size of graph datasets present significant challenges for analysis and computation. In response, graph reduction techniques have gained prominence for simplifying large graphs while preserving essential properties. In this survey, we aim to provide a comprehensive understanding of graph reduction methods, including graph sparsification, graph coarsening, and graph condensation. Specifically, we establish a unified definition for these methods and introduce a hierarchical taxonomy to categorize the challenges they address. Our survey then systematically reviews the technical details of these methods and emphasizes their practical applications across diverse scenarios. Furthermore, we outline critical research directions to ensure the continued effectiveness of graph reduction techniques.

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EAAI Journal 2024 Journal Article

A novel method based on near-infrared imaging spectroscopy and graph-learning to evaluate the dyeing uniformity of polyester yarn

Zheng Liu
Shiluo Huang
Wei Jin
Ying Mu

The quality of polyester yarn is mainly affected by its dyeing uniformity. As a result, textile manufacturers need to inspect the dyeing uniformity of polyester yarn. The existing inspection methods rely on the dyeing process which can be viewed as a way to extract the discriminative information of the polyester yarns with different dyeing uniformities. However, the dyeing process is extremely time-consuming and laborious. It is very expensive to obtain the dyeing uniformities of polyester yarns, due to the inefficient dyeing process. To address this problem, an efficient and effective inspection method based on near-infrared imaging spectroscopy and semi-supervised learning is introduced in this paper. This introduced method can obtain the dyeing uniformity of polyester yarn without the dyeing process, which greatly boosts the efficiency of inspection. In the developed method, to acquire the representative information without the dyeing process, near-infrared spectra are applied to represent the molecular structure differences of different polyester yarns. Considering that the dyeing uniformities are expensive to obtain, a novel semi-supervised learning algorithm, termed consensus local graph based on multiple kernel learning (CLGMKL), is proposed in the introduced method to evaluate the dyeing uniformity of polyester yarn. Since redundant wavebands and noise exist in the collected spectra, the relationship among spectra in CLGMKL is learned based on the extracted low-rank matrices which contain the discriminative information of base kernels. Besides, CLGMKL incorporates the label information of spectra. Finally, extensive experiments on the collected spectra of polyester yarns demonstrate the superiority of the proposed method.

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EAAI Journal 2024 Journal Article

CCNet: Collaborative Camouflaged Object Detection via decoder-induced information interaction and supervision refinement network

Cong Zhang
Hongbo Bi
Disen Mo
Weihan Sun
Jinghui Tong
Wei Jin
Yongqiang Sun

Recently, research based on the camouflaged object detection (COD) task has achieved great progress, while the collaborative camouflaged object detection (CoCOD) task is still lacking. Our research focuses on the simultaneous detection and localization of the collaborative camouflaged objects, i. e. , CoCOD task. We use the cooperative information between a single image and a group of camouflage images to discover cooperative camouflaged objects effectively. In this paper, we propose a collaborative cross-scale feature learning network (CCNet). Our model is characterized by two innovative constructions: We proposed an edge augmentation module (EAM), which effectively extracts edge information of the camouflaged object and integrates it with the collaborative information employed in auxiliary supervision. In addition, we design a group decoder module (GDM) to extract and merge co-camouflage information. Extensive experiments on CoCOD8K datasets demonstrate that our CCNet significantly outperforms the existing 13 state-of-the-art COD and 6 state-of-the-art collaborative salient object detection (CoSOD) methods under six widely used evaluation metrics. The code will be available at: https: //github. com/zc199823/CCNet--CoCOD.

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TIST Journal 2024 Journal Article

Deep Learning in Single-cell Analysis

Dylan Molho
Jiayuan Ding
Wenzhuo Tang
Zhaoheng Li
Hongzhi Wen
Yixin Wang
Julian Venegas
Wei Jin

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high dimensional, sparse, and heterogeneous and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.

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AAAI Conference 2024 Conference Paper

Deep Learning on Graphs: A Data-Centric Exploration

Wei Jin

Many learning tasks in Artificial Intelligence (AI) require dealing with graph data, ranging from biology and chemistry to finance and education. As powerful deep learning tools for graphs, graph neural networks (GNNs) have demonstrated remarkable performance in various graph-related applications. Despite the significant accomplishments of GNNs, recent studies have highlighted that their efficiency and effectiveness face significant challenges such as adversarial robustness and scalability, which are fundamentally linked to data. While major attention has been devoted to improving GNNs from the model perspective, the potential of directly enhancing data has often been overlooked. It underscores a critical gap in GNN research---while model improvements are undoubtedly important, we also need to recognize and address the data-related factors contributing to the challenges. Hence, my research is to investigate solutions for these challenges from the data perspective, employing strategies such as data characterization, reduction, augmentation, transformation, and detection.

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NeurIPS Conference 2024 Conference Paper

Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Zhikai Chen
Haitao Mao
Jingzhe Liu
Yu Song
Bingheng Li
Wei Jin
Bahare Fatemi
Anton Tsitsulin

Given the ubiquity of graph data and its applications in diverse domains, building a Graph Foundation Model (GFM) that can work well across different graphs and tasks with a unified backbone has recently garnered significant interests. A major obstacle to achieving this goal stems from the fact that graphs from different domains often exhibit diverse node features. Inspired by multi-modal models that align different modalities with natural language, the text has recently been adopted to provide a unified feature space for diverse graphs. Despite the great potential of these text-space GFMs, current research in this field is hampered by two problems. First, the absence of a comprehensive benchmark with unified problem settings hinders a clear understanding of the comparative effectiveness and practical value of different text-space GFMs. Second, there is a lack of sufficient datasets to thoroughly explore the methods' full potential and verify their effectiveness across diverse settings. To address these issues, we conduct a comprehensive benchmark providing novel text-space datasets and comprehensive evaluation under unified problem settings. Empirical results provide new insights and inspire future research directions. Our code and data are publicly available from https: //github. com/CurryTang/TSGFM.

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EAAI Journal 2023 Journal Article

A deep multi-instance neural network for dyeing-free inspection of yarn dyeing uniformity

Shiluo Huang
Zheng Liu
Wei Jin
Ying Mu

Dyeing evenness is a decisive factor influencing the commercial values of polyester yarn. Thus, the inspection of yarn dyeing uniformity plays a vital role in the textile community. Due to the inefficiency of dyeing process, the traditional dyeing-based inspection methods are both laborious and time-consuming. To improve efficiency, this study attempts to develop a fast dyeing-free method for dyeing uniformity inspection based on imaging spectrometer and multi-instance learning (MIL). The relevant properties of yarn samples are first recorded in the hyperspectral images (HSIs). As the available labels are ambiguous, classifying the collected HSIs has become a MIL problem. Meanwhile, the correlation between the spectral pixels and the sample labels might be sophisticated. It might be difficult for the existing MIL methods to learn such data. In this paper, a deep Fisher score-based multi-instance neural network (DFSNet) is also proposed for classifying the captured HSIs. The DFSNet is able to learn a sophisticated correlation between deep instance features and bag representation. Specifically, a Fisher score-based MIL pooling layer is first developed to convert the instance-level features into bag-level features. The DFSNet is then developed with a ladder structure and the Fisher score-based MIL pooling layer. The proposed dyeing-free method and DFSNet are evaluated using the actual polyester samples. Moreover, the proposed DFSNet and the spectral range of HSI are further analyzed. The experiment results have indicated that the proposed method could achieve satisfactory performance, providing a potential solution to fast dyeing uniformity inspection.

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AAAI Conference 2023 Short Paper

ACCD: An Adaptive Clustering-Based Collusion Detector in Crowdsourcing (Student Abstract)

Ruoyu Xu
Gaoxiang Li
Wei Jin
Austin Chen
Victor S. Sheng

Crowdsourcing is a popular method for crowd workers to collaborate on tasks. However, workers coordinate and share answers during the crowdsourcing process. The term for this is "collusion". Copies from others and repeated submissions are detrimental to the quality of the assignments. The majority of the existing research on collusion detection is limited to ground truth problems (e.g., labeling tasks) and requires a predetermined threshold to be established in advance. In this paper, we aim to detect collusion behavior of workers in an adaptive way, and propose an Adaptive Clustering Based Collusion Detection approach (ACCD) for a broad range of task types and data types solved via crowdsourcing (e.g., continuous rating with or without distributions). Extensive experiments on both real-world and synthetic datasets show the superiority of ACCD over state-of-the-art approaches.

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NeurIPS Conference 2023 Conference Paper

Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation

Wei Jin
Haitao Mao
Zheng Li
Haoming Jiang
Chen Luo
Hongzhi Wen
Haoyu Han
Hanqing Lu

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences is essential for providing personalized recommendations. Session-based recommendation, which utilizes customer session data to predict their next interaction, has become increasingly popular. However, existing session datasets have limitations in terms of item attributes, user diversity, and dataset scale. As a result, they cannot comprehensively capture the spectrum of user behaviors and preferences. To bridge this gap, we present the Amazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales, where the major languages of products are English, German, Japanese, French, Italian, and Spanish. Remarkably, the dataset can help us enhance personalization and understanding of user preferences, which can benefit various existing tasks as well as enable new tasks. To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation. With the above tasks, we benchmark a range of algorithms on our proposed dataset, drawing new insights for further research and practice. In addition, based on the proposed dataset and tasks, we hosted a competition in the KDD CUP 2023 https: //www. aicrowd. com/challenges/amazon-kdd-cup-23-multilingual-recommendation-challenge and have attracted thousands of users and submissions. The winning solutions and the associated workshop can be accessed at our website~https: //kddcup23. github. io/.

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NeurIPS Conference 2023 Conference Paper

Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?

Haitao Mao
Zhikai Chen
Wei Jin
Haoyu Han
Yao Ma
Tong Zhao
Neil Shah
Jiliang Tang

Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs. Notably, most real-world homophilic and heterophilic graphs are comprised of a mixture of nodes in both homophilic and heterophilic structural patterns, exhibiting a structural disparity. However, the analysis of GNN performance with respect to nodes exhibiting different structural patterns, e. g. , homophilic nodes in heterophilic graphs, remains rather limited. In the present study, we provide evidence that Graph Neural Networks(GNNs) on node classification typically perform admirably on homophilic nodes within homophilic graphs and heterophilic nodes within heterophilic graphs while struggling on the opposite node set, exhibiting a performance disparity. We theoretically and empirically identify effects of GNNs on testing nodes exhibiting distinct structural patterns. We then propose a rigorous, non-i. i. d PAC-Bayesian generalization bound for GNNs, revealing reasons for the performance disparity, namely the aggregated feature distance and homophily ratio difference between training and testing nodes. Furthermore, we demonstrate the practical implications of our new findings via (1) elucidating the effectiveness of deeper GNNs; and (2) revealing an over-looked distribution shift factor on graph out-of-distribution problem and proposing a new scenario accordingly.

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IROS Conference 2022 Conference Paper

A Deep-Learning-based System for Indoor Active Cleaning

Yike Yun
Linjie Hou
Zijian Feng
Wei Jin
Yang Liu
Heng Wang
Ruonan He
Weitao Guo

Cleaning public areas like commercial complexes is challenging due to their sophisticated surroundings and the vast kinds of real-life dirt. Robots are required to distinguish dirts and apply corresponding cleaning strategies. In this work, we proposed an active-cleaning framework by utilizing deep-learning methods for both solid wastes detection and liquid stains segmentation. Our system consists of 4 components: a Perception module integrated with deep-learning models, a Post-processing module for projection, a Tracking module for map localization, and a Planning and Control module for cleaning strategies. Compared with classic approaches, our vision-based system significantly improves cleaning efficiency. Besides, we released the largest real-world indoor hybrid dirt cleaning dataset (HD10K) containing 10K labeled images, together with a track-level evaluation metric for better cleaning performance measurement. The proposed deep-learning based system is verified with extensive experiments on our dataset, and deployed to Gaussian Robotics's robots operating globally. Dataset is available at: https://gaussianopensource.github.io/projects/active_cleaning.

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AAAI Conference 2021 System Paper

DeepRobust: a Platform for Adversarial Attacks and Defenses

Yaxin Li
Wei Jin
Han Xu
Jiliang Tang

DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with Deep- Robust. In this paper, we introduce the functions of Deep- Robust with detailed instructions. We will demonstrate that DeepRobust is a useful tool to measure deep learning model robustness and to identify the suitable countermeasures against adversarial attacks. The platform is kept updated and can be found at https: //github. com/DSE-MSU/DeepRobust. More details of instructions can be found in the documentation at https: //deeprobust. readthedocs. io/en/latest/.

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NeurIPS Conference 2021 Conference Paper

Graph Neural Networks with Adaptive Residual

Xiaorui Liu
Jiayuan Ding
Wei Jin
Han Xu
Yao Ma
Zitao Liu
Jiliang Tang

Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks. In this work, we discover an interesting phenomenon that although residual connections in the message passing of GNNs help improve the performance, they immensely amplify GNNs' vulnerability against abnormal node features. This is undesirable because in real-world applications, node features in graphs could often be abnormal such as being naturally noisy or adversarially manipulated. We analyze possible reasons to understand this phenomenon and aim to design GNNs with stronger resilience to abnormal features. Our understandings motivate us to propose and derive a simple, efficient, interpretable, and adaptive message passing scheme, leading to a novel GNN with Adaptive Residual, AirGNN. Extensive experiments under various abnormal feature scenarios demonstrate the effectiveness of the proposed algorithm.

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AAAI Conference 2018 Short Paper

Learning Feature Representations for Keyphrase Extraction

Corina Florescu
Wei Jin

In supervised approaches for keyphrase extraction, a candidate phrase is encoded with a set of hand-crafted features and machine learning algorithms are trained to discriminate keyphrases from non-keyphrases. Although the manuallydesigned features have shown to work well in practice, feature engineering is a difﬁcult process that requires expert knowledge and normally does not generalize well. In this paper, we present SurfKE, a feature learning framework that exploits the text itself to automatically discover patterns that keyphrases exhibit. Our model represents the document as a graph and automatically learns feature representation of phrases. The proposed model obtains remarkable improvements in performance over strong baselines.

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TIST Journal 2012 Journal Article

EachWiki

Haofen Wang
Linyun Fu
Wei Jin
Yong Yu

Wikipedia, one of the best-known wikis and the world’s largest free online encyclopedia, has embraced the power of collaborative editing to harness collective intelligence. However, using such a wiki to create high-quality articles is not as easy as people imagine, given for instance the difficulty of reusing knowledge already available in Wikipedia. As a result, the heavy burden of upbuilding and maintaining the ever-growing online encyclopedia still rests on a small group of people. In this article, we aim at facilitating wiki authoring by providing annotation recommendations, thus lightening the burden of both contributors and administrators. We leverage the collective wisdom of the users by exploiting Semantic Web technologies with Wikipedia data and adopt a unified algorithm to support link, category, and semantic relation recommendation. A prototype system named EachWiki is proposed and evaluated. The experimental results show that it has achieved considerable improvements in terms of effectiveness, efficiency and usability. The proposed approach can also be applied to other wiki-based collaborative editing systems.

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TCS Journal 2012 Journal Article

Generic subset ranking using binary classifiers

Zhengya Sun
Wei Jin
Jue Wang

A widespread idea to attack the ranking problem is by reducing it into a set of binary preferences and applying well studied classification methods. In particular, we consider this reduction for generic subset ranking, which is based on minimization of position-sensitive loss functions. The basic question addressed in this paper relates to whether an accurate classifier would transfer directly into a good ranker. We propose a consistent reduction framework guaranteeing that the minimal regret of zero for subset ranking is achievable by learning binary preferences assigned with importance weights. This fact allows us to further develop a novel upper bound on the subset ranking regret in terms of binary regrets. We show that their ratio can be at most 2 times the maximal deviation of discounts between adjacent positions. We also present a refined version of this bound when only the quality over the top rank positions is of concern. These bounds provide theoretical support on the use of the resulting binary classifiers for solving the subset ranking problem.

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