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Ling Huang

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19 papers

2 author rows

IJCAI Conference 2025 Conference Paper

DPMamba: Distillation Prompt Mamba for Multimodal Remote Sensing Image Classification with Missing Modalities

Yueguang Yang
Jiahui Qu
Ling Huang
Wenqian Dong

Multimodal remote sensing image classification (RSIC) has emerged as a key focus in Earth observation, driven by its capacity to extract complementary information from diverse sources. Existing methods struggle with modality absence caused by weather or equipment failures, leading to performance degradation. As a solution, knowledge distillation-based methods train student networks (SN) using a full-modality teacher, but they usually require training separate SN for each modality absence scenario, increasing complexity. To this end, we propose a unified Distillation Prompt Mamba (DPMamba) framework for multimodal RSIC with missing modalities. DPMamba leverages knowledge distillation in a shared text semantic space to optimize learnable prompts, transforming them from ``placeholder" to ``adaptation" states by enriching missing modality information with full-modality knowledge. To achieve this, we focus on two main aspects: first, we propose a new modality-aware Mamba for dynamically and hierarchically extracting cross-modality interactive features, providing richer, contextually relevant representations for backpropagation-based optimization of prompts; and second, we introduce a novel text-bridging distillation method to efficiently transfer full-modality knowledge, guiding the inclusion of missing modality information into prompts. Extensive evaluations demonstrate the effectiveness and robustness of the proposed DPMamba.

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JBHI Journal 2025 Journal Article

Improving Clinical Foundation Models with Multi-modal Learning and Domain Adaptation for Chronic Disease Prediction

Wenhui Hou
Jianqiang Wang
Qika Lin
Xiaokang Wang
Ling Huang

Modelling patient trajectories from longitudinal electronic health records (EHRs) is crucial for early chronic disease prediction. Foundation models (FMs), benefiting from the computational power and generalization abilities, offer a promising direction towards understanding patient health progression. However, key challenges of adopting FMs in clinical decisions remain in (1) incorporating multi-modal EHR data into an FM effectively for unified patient representations and (2) ensuring model generalizability across various clinical domains with distribution shifts. To address these challenges, we propose MsHeCare, an FM-based, two-stage learning framework integrating multi-modal representation learning and multi-source domain adaptation (MSDA). In the pretraining stage, MsHeCare performs self-supervised contrastive learning and masked language modelling tasks to mitigate semantic biases across text-based diagnostic and treatment sequences. A cross-attention mechanism further fuses these temporal features with structured static demographic information, enhancing personalized patient representations. In the fine-tuning stage, MsHeCare incorporates an MSDA framework with a novel source importance estimation method, facilitating adaptive knowledge transfer across domains and improving model generalizability. Experiments on two real-world EHR datasets demonstrate that MsHeCare significantly outperforms single-domain baselines and state-of-the-art MSDA methods. Furthermore, we validate the robustness of MsHeCare across varying targetdomain data sizes and demonstrate its alignment with clinical practices through case studies, underscoring its potential for multi-modal, domain-adaptive predictive healthcare systems.

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YNIMG Journal 2022 Journal Article

An awareness-dependent mapping of saliency in the human visual system

Lijuan Wang
Ling Huang
Mengsha Li
Xiaotong Wang
Shiyu Wang
Yuefa Lin
Xilin Zhang

The allocation of exogenously cued spatial attention is governed by a saliency map. Yet, how salience is mapped when multiple salient stimuli are present simultaneously, and how this mapping interacts with awareness remains unclear. These questions were addressed here using either visible or invisible displays presenting two foreground stimuli (whose bars were oriented differently from the bars in the otherwise uniform background): a high salience target and a distractor of varied, lesser salience. Interference, or not, by the distractor with the effective salience of the target served to index a graded or non-graded nature of salience mapping, respectively. The invisible and visible displays were empirically validated by a two-alternative forced choice test (detecting the quadrant of the target) demonstrating subjects' performance at or above chance level, respectively. By combining psychophysics, fMRI, and effective connectivity analysis, we found a graded distribution of salience with awareness, changing to a non-graded distribution without awareness. Crucially, we further revealed that the graded distribution was contingent upon feedback from the posterior intraparietal sulcus (pIPS, especially from the right pIPS), whereas the non-graded distribution was innate to V1. Together, this awareness-dependent mapping of saliency reconciles several previous, seemingly contradictory findings regarding the nature of the saliency map.

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TIST Journal 2021 Journal Article

Temporal Hierarchical Graph Attention Network for Traffic Prediction

Ling Huang
Xing-Xing Liu
Shu-Qiang Huang
Chang-Dong Wang
Wei Tu
Jia-Meng Xie
Shuai Tang
Wendi Xie

As a critical task in intelligent traffic systems, traffic prediction has received a large amount of attention in the past few decades. The early efforts mainly model traffic prediction as the time-series mining problem, in which the spatial dependence has been largely ignored. As the rapid development of deep learning, some attempts have been made in modeling traffic prediction as the spatio-temporal data mining problem in a road network, in which deep learning techniques can be adopted for modeling the spatial and temporal dependencies simultaneously. Despite the success, the spatial and temporal dependencies are only modeled in a regionless network without considering the underlying hierarchical regional structure of the spatial nodes, which is an important structure naturally existing in the real-world road network. Apart from the challenge of modeling the spatial and temporal dependencies like the existing studies, the extra challenge caused by considering the hierarchical regional structure of the road network lies in simultaneously modeling the spatial and temporal dependencies between nodes and regions and the spatial and temporal dependencies between regions. To this end, this article proposes a new Temporal Hierarchical Graph Attention Network (TH-GAT). The main idea lies in augmenting the original road network into a region-augmented network, in which the hierarchical regional structure can be modeled. Based on the region-augmented network, the region-aware spatial dependence model and the region-aware temporal dependence model can be constructed, which are two main components of the proposed TH-GAT model. In addition, in the region-aware spatial dependence model, the graph attention network is adopted, in which the importance of a node to another node, of a node to a region, of a region to a node, and of a region to another region, can be captured automatically by means of the attention coefficients. Extensive experiments are conducted on two real-world traffic datasets, and the results have confirmed the superiority of the proposed TH-GAT model.

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AAAI Conference 2020 Conference Paper

Multi-View Clustering in Latent Embedding Space

Man-Sheng Chen
Ling Huang
Chang-Dong Wang
Dong Huang

Previous multi-view clustering algorithms mostly partition the multi-view data in their original feature space, the efﬁcacy of which heavily and implicitly relies on the quality of the original feature presentation. In light of this, this paper proposes a novel approach termed Multi-view Clustering in Latent Embedding Space (MCLES), which is able to cluster the multi-view data in a learned latent embedding space while simultaneously learning the global structure and the cluster indicator matrix in a uniﬁed optimization framework. Specifically, in our framework, a latent embedding representation is ﬁrstly discovered which can effectively exploit the complementary information from different views. The global structure learning is then performed based on the learned latent embedding representation. Further, the cluster indicator matrix can be acquired directly with the learned global structure. An alternating optimization scheme is introduced to solve the optimization problem. Extensive experiments conducted on several real-world multi-view datasets have demonstrated the superiority of our approach.

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AAAI Conference 2020 Conference Paper

MuMod: A Micro-Unit Connection Approach for Hybrid-Order Community Detection

Ling Huang
Hong-Yang Chao
Quangqiang Xie

In the past few years, higher-order community detection has drawn an increasing amount of attention. Compared with the lower-order approaches that rely on the connectivity pattern of individual nodes and edges, the higher-order approaches discover communities by leveraging the higher-order connectivity pattern via constructing a motif-based hypergraph. Despite success in capturing the building blocks of complex networks, recent study has shown that the higher-order approaches unavoidably suffer from the hypergraph fragmentation issue. Although an edge enhancement strategy has been designed previously to address this issue, adding additional edges may corrupt the original lower-order connectivity pattern. To this end, this paper deﬁnes a new problem of community detection, namely hybrid-order community detection, which aims to discover communities by simultaneously leveraging the lower-order connectivity pattern and the higherorder connectivity pattern. For addressing this new problem, a new Micro-unit Modularity (MuMod) approach is designed. The basic idea lies in constructing a micro-unit connection network, where both of the lower-order connectivity pattern and the higher-order connectivity pattern are utilized. And then a new micro-unit modularity model is proposed for generating the micro-unit groups, from which the overlapping community structure of the original network can be derived. Extensive experiments are conducted on ﬁve real-world networks. Comparison results with twelve existing approaches conﬁrm the effectiveness of the proposed method.

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IJCAI Conference 2019 Conference Paper

BPAM: Recommendation Based on BP Neural Network with Attention Mechanism

Wu-Dong Xi
Ling Huang
Chang-Dong Wang
Yin-Yu Zheng
Jianhuang Lai

Inspired by the significant success of deep learning, some attempts have been made to introduce deep neural networks (DNNs) in recommendation systems to learn users' preferences for items. Since DNNs are well suitable for representation learning, they enable recommendation systems to generate more accurate prediction. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily lead to over-fitting. To tackle these problems, we propose a novel recommendation algorithm based on Back Propagation (BP) neural network with Attention Mechanism (BPAM). In particular, the BP neural network is utilized to learn the complex relationship of the target users and their neighbors. Compared with deep neural network, the shallow neural network, i. e. , BP neural network, can not only reduce the computational and storage costs, but also prevent the model from over-fitting. In addition, an attention mechanism is designed to capture the global impact on all nearest target users for each user. Extensive experiments on eight benchmark datasets have been conducted to evaluate the effectiveness of the proposed model.

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AAAI Conference 2019 Conference Paper

DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System

Zhi-Hong Deng
Ling Huang
Chang-Dong Wang
Jian-Huang Lai
Philip S. Yu

In general, recommendation can be viewed as a matching problem, i. e. , match proper items for proper users. However, due to the huge semantic gap between users and items, it’s almost impossible to directly match users and items in their initial representation spaces. To solve this problem, many methods have been studied, which can be generally categorized into two types, i. e. , representation learning-based CF methods and matching function learning-based CF methods. Representation learning-based CF methods try to map users and items into a common representation space. In this case, the higher similarity between a user and an item in that space implies they match better. Matching function learning-based CF methods try to directly learn the complex matching function that maps user-item pairs to matching scores. Although both methods are well developed, they suffer from two fundamental flaws, i. e. , the limited expressiveness of dot product and the weakness in capturing low-rank relations respectively. To this end, we propose a general framework named DeepCF, short for Deep Collaborative Filtering, to combine the strengths of the two types of methods and overcome such flaws. Extensive experiments on four publicly available datasets demonstrate the effectiveness of the proposed DeepCF framework.

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AAAI Conference 2019 Short Paper

Higher-Order Multi-Layer Community Detection

Ling Huang
Chang-Dong Wang
Hong-Yang Chao

In this paper, we define a new problem of multi-layer network community detection, namely higher-order multi-layer community detection. A multi-layer motif (M-Motif) approach is proposed, which discovers communities with good intralayer higher-order community quality while preserving interlayer higher-order community consistency. Experimental results have confirmed the superiority of the proposed method.

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IJCAI Conference 2019 Conference Paper

Unsupervised Multi-view Learning

Ling Huang

Unsupervised multi-view learning is a hot research topic. The main challenge lies in how to integrate information from different views to enhance the unsupervised learning performance. In this paper, we present our research works on multi-view data clustering and multi-view network community detection respectively. The main contributions are summarized by emphasizing the challenges we have addressed. In addition, the ongoing work and the future work are briefly presented.

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TIST Journal 2014 Journal Article

Joint Link Prediction and Attribute Inference Using a Social-Attribute Network

Neil Zhenqiang Gong
Ameet Talwalkar
Lester Mackey
Ling Huang
Eui Chul Richard Shin
Emil Stefanov
Elaine (Runting) Shi
Dawn Song

The effects of social influence and homophily suggest that both network structure and node-attribute information should inform the tasks of link prediction and node-attribute inference. Recently, Yin et al. [2010a, 2010b] proposed an attribute-augmented social network model, which we call Social-Attribute Network (SAN), to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with a restart algorithm to the SAN framework and showed improved performance. In this article, we extend the SAN framework with several leading supervised and unsupervised link-prediction algorithms and demonstrate performance improvement for each algorithm on both link prediction and attribute inference. Moreover, we make the novel observation that attribute inference can help inform link prediction, that is, link-prediction accuracy is further improved by first inferring missing attributes. We comprehensively evaluate these algorithms and compare them with other existing algorithms using a novel, large-scale Google+ dataset, which we make publicly available (http://www.cs.berkeley.edu/~stevgong/gplus.html).

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NeurIPS Conference 2014 Conference Paper

Large-Margin Convex Polytope Machine

Alex Kantchelian
Michael Tschantz
Ling Huang
Peter Bartlett
Anthony Joseph
J. D. Tygar

We present the Convex Polytope Machine (CPM), a novel non-linear learning algorithm for large-scale binary classification tasks. The CPM finds a large margin convex polytope separator which encloses one class. We develop a stochastic gradient descent based algorithm that is amenable to massive datasets, and augment it with a heuristic procedure to avoid sub-optimal local minima. Our experimental evaluations of the CPM on large-scale datasets from distinct domains (MNIST handwritten digit recognition, text topic, and web security) demonstrate that the CPM trains models faster, sometimes several orders of magnitude, than state-of-the-art similar approaches and kernel-SVM methods while achieving comparable or better classification performance. Our empirical results suggest that, unlike prior similar approaches, we do not need to control the number of sub-classifiers (sides of the polytope) to avoid overfitting.

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JMLR Journal 2012 Journal Article

Query Strategies for Evading Convex-Inducing Classifiers

Blaine Nelson
Benjamin I. P. Rubinstein
Ling Huang
Anthony D. Joseph
Steven J. Lee
Satish Rao
J. D. Tygar

Classifiers are often used to detect miscreant activities. We study how an adversary can systematically query a classifier to elicit information that allows the attacker to evade detection while incurring a near-minimal cost of modifying their intended malfeasance. We generalize the theory of Lowd and Meek (2005) to the family of convex-inducing classifiers that partition their feature space into two sets, one of which is convex. We present query algorithms for this family that construct undetected instances of approximately minimal cost using only polynomially-many queries in the dimension of the space and in the level of approximation. Our results demonstrate that near-optimal evasion can be accomplished for this family without reverse engineering the classifier's decision boundary. We also consider general l p costs and show that near-optimal evasion on the family of convex-inducing classifiers is generally efficient for both positive and negative convexity for all levels of approximation if p=1. [abs] [ pdf ][ bib ] &copy JMLR 2012. ( edit, beta )

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UAI Conference 2010 Conference Paper

Online Semi-Supervised Learning on Quantized Graphs

Michal Valko
Branislav Kveton
Ling Huang
Daniel Ting

In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local “representative points” that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We apply our algorithm to face recognition and optical character recognition applications to show that we can take advantage of the manifold structure to outperform the previous methods. Unlike previous heuristic approaches, we show that our method yields provable performance bounds.

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NeurIPS Conference 2010 Conference Paper

Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression

Ling Huang
Jinzhu Jia
Bin Yu
Byung-Gon Chun
Petros Maniatis
Mayur Naik

Predicting the execution time of computer programs is an important but challenging problem in the community of computer systems. Existing methods require experts to perform detailed analysis of program code in order to construct predictors or select important features. We recently developed a new system to automatically extract a large number of features from program execution on sample inputs, on which prediction models can be constructed without expert knowledge. In this paper we study the construction of predictive models for this problem. We propose the SPORE (Sparse POlynomial REgression) methodology to build accurate prediction models of program performance using feature data collected from program execution on sample inputs. Our two SPORE algorithms are able to build relationships between responses (e. g. , the execution time of a computer program) and features, and select a few from hundreds of the retrieved features to construct an explicitly sparse and non-linear model to predict the response variable. The compact and explicitly polynomial form of the estimated model could reveal important insights into the computer program (e. g. , features and their non-linear combinations that dominate the execution time), enabling a better understanding of the program’s behavior. Our evaluation on three widely used computer programs shows that SPORE methods can give accurate prediction with relative error less than 7% by using a moderate number of training data samples. In addition, we compare SPORE algorithms to state-of-the-art sparse regression algorithms, and show that SPORE methods, motivated by real applications, outperform the other methods in terms of both interpretability and prediction accuracy.

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NeurIPS Conference 2008 Conference Paper

Spectral Clustering with Perturbed Data

Ling Huang
Donghui Yan
Nina Taft
Michael Jordan

Spectral clustering is useful for a wide-ranging set of applications in areas such as biological data analysis, image processing and data mining. However, the computational and/or communication resources required by the method in processing large-scale data sets are often prohibitively high, and practitioners are often required to perturb the original data in various ways (quantization, downsampling, etc) before invoking a spectral algorithm. In this paper, we use stochastic perturbation theory to study the effects of data perturbation on the performance of spectral clustering. We show that the error under perturbation of spectral clustering is closely related to the perturbation of the eigenvectors of the Laplacian matrix. From this result we derive approximate upper bounds on the clustering error. We show that this bound is tight empirically across a wide range of problems, suggesting that it can be used in practical settings to determine the amount of data reduction allowed in order to meet a specification of permitted loss in clustering performance.

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YNIMG Journal 2006 Journal Article

76-Space analysis of grey matter diffusivity: Methods and applications

Tianming Liu
Geoffrey Young
Ling Huang
Nan-Kuei Chen
Stephen T.C. Wong

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) allow in vivo investigation of molecular motion of tissue water at a microscopic level in cerebral gray matter (GM) and white matter (WM). DWI/DTI measure of water diffusion has been proven to be invaluable for the study of many neurodegenerative diseases (e. g. , Alzheimer's disease and Creutzfeldt–Jakob disease) that predominantly involve GM. Thus, quantitative analysis of GM diffusivity is of scientific interest and is promised to have a clinical impact on the investigation of normal brain aging and neuropathology. In this paper, we propose an automated framework for analysis of GM diffusivity in 76 standard anatomic subdivisions of gray matter to facilitate studies of neurodegenerative and other gray matter neurological diseases. The computational framework includes three enabling technologies: (1) automatic parcellation of structural MRI GM into 76 precisely defined neuroanatomic subregions (“76-space”), (2) automated segmentation of GM, WM and CSF based on DTI data, and (3) automatic measurement of the average apparent diffusion coefficient (ADC) in each segmented GM subregion. We evaluate and validate this computational framework for 76-space GM diffusivity analysis using data from normal volunteers and from patients with Creutzfeldt–Jakob disease.

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NeurIPS Conference 2006 Conference Paper

In-Network PCA and Anomaly Detection

Ling Huang
XuanLong Nguyen
Minos Garofalakis
Michael Jordan
Anthony Joseph
Nina Taft

We consider the problem of network anomaly detection in large distributed systems. In this setting, Principal Component Analysis (PCA) has been proposed as a method for discover- ing anomalies by continuously tracking the projection of the data onto a residual subspace. This method was shown to work well empirically in highly aggregated networks, that is, those with a limited number of large nodes and at coarse time scales. This approach, how- ever, has scalability limitations. To overcome these limitations, we develop a PCA-based anomaly detector in which adaptive local data (cid: 2)lters send to a coordinator just enough data to enable accurate global detection. Our method is based on a stochastic matrix perturba- tion analysis that characterizes the tradeoff between the accuracy of anomaly detection and the amount of data communicated over the network.

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ICRA Conference 1999 Conference Paper

Force-Responsive Robotic Assembly of Transmission Components

Wyatt S. Newman
Michael S. Branicky
Andy Podgurski
Siddharth R. Chhatpar
Ling Huang
Jayendran Swaminathan
Hao Zhang

Assembly tasks involving large position uncertainties are unsuitable for use of position-controlled robots. To automate such tasks, the assembly system must be responsive to contact forces. Issues in addressing force-responsive automated assembly include contact stability, the degree of force responsiveness required for success, the speed of a successful implementation, and the means to program a force-responsive system to perform a given assembly task. We examine these issues for robotic assembly in the context of automotive transmission components. We report on an impedance-based low-level algorithm and its interface to higher-level strategies that exhibits gentle, fast and reliable assembly of our example components.

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