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- DeepMove: Predicting Human Mobility with Attentional Recurrent Networks841
In DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories, a multi-modal embedding recurrent neural network is designed to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility.
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- Bridging Collaborative Filtering and Semi-Supervised Learning316
This work proposes to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs.
- Exploiting internal and external semantics for the clustering of short texts using world knowledge269
The proposed method employs a hierarchical three-level structure to tackle the data sparsity problem of original short texts and reconstruct the corresponding feature space with the integration of multiple semantic knowledge bases -- Wikipedia and WordNet.
- Emergence of Indigenous Artemisinin-Resistant Plasmodium falciparum in Africa265
Resistance to artemisinin resistance in Plasmodium falciparum is observed in a patient who was working in Equatorial Guinea.
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- Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification206
This paper addresses the missed contextual cues by exploiting both the accurate human parts and the coarse non-human parts by applying a human parsing model and a self-attention mechanism to capture the soft latent (non-human) part masks.
- Weakly-Supervised Neural Text Classification203
This paper proposes a weakly-supervised method that addresses the lack of training data in neural text classification and achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.
- Text Classification Using Label Names Only: A Language Model Self-Training Approach196
This paper uses pre-trained neural language models both as general linguistic knowledge sources for category understanding and as representation learning models for document classification, and achieves around 90% accuracy on four benchmark datasets.
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- GMove158
This work proposes GMove, a group-level mobility modeling method using GeoSM data that alternates between user grouping and mobility modeling, and generates an ensemble of Hidden Markov Models (HMMs) to characterize group- level movement regularity.
- Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach153
This work develops a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision, underpinned by contrastive regularization and confidence-based reweighting, which gradually improves model fitting while effectively suppressing error propagation.
- Regions, Periods, Activities150
CrossMap is presented, a novel cross-modal representation learning method that uncovers urban dynamics with massive GTSM data and significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency.
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- GeoBurst148
GeoBurst is a method that enables effective and real-time local event detection from geo-tagged tweet streams and significantly outperforms state-of-the-art methods in precision, and is orders of magnitude faster.
- Robust solution of monotone stochastic linear complementarity problems146
A new error bound for the monotone LCP is given and used to show that solutions of the ERM formulation are robust in the sense that they may have a minimum sensitivity with respect to random parameter variations in SLCP.
- Splitter144
This work proposes Splitter to effectively mine fine-grained sequential patterns, which must satisfy spatial compactness, semantic consistency and temporal continuity simultaneously, and uses an effective algorithm called weighted snippet shift to detect clusters, and leverages a divide-and-conquer strategy to speed up the top-down pattern splitting process.
- Weakly-Supervised Hierarchical Text Classification142
This paper proposes a weakly-supervised neural method for hierarchical text classification that features a hierarchical neural structure, which mimics the given hierarchy and is capable of determining the proper levels for documents with a blocking mechanism.
- TaxoGen140
The method, TaxoGen, uses term embeddings and hierarchical clustering to construct a topic taxonomy in a recursive fashion and consists of an adaptive spherical clustering module for allocating terms to proper levels when splitting a coarse topic into fine-grained ones.
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- Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks126
The HEER algorithm is proposed, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics, and demonstrates the effectiveness of the proposed HEER model and the utility of edge representations and heterogeneity metrics.
- TrioVecEvent123
Crowdourcing is used to evaluate TrioVecEvent, a method that leverages multimodal embeddings to achieve accurate online local event detection and introduces discriminative features that can well characterize local events.
- Identifying video spammers in online social networks116
This paper builds a large test collection of YouTube users, and applies machine learning to provide a heuristic for classifying an arbitrary video as either legitimate or spam, and shows that this approach succeeds at detecting much of the spam while only falsely classifying a small percentage of the legitimate videos as spam.
- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection110
This work proposes a hierarchical trinity search framework to simultaneously discover efficient architectures for all components of object detector in an end-to-end manner and empirically reveals that different parts of the detector prefer different operators.
- SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup107
This work proposes a simple but effective data augmentation method to improve the label efficiency of active sequence labeling, SeqMix, which simply augments the queried samples by generating extra labeled sequences in each iteration.
- Evaluating geo-social influence in location-based social networks104
This paper performs an in-depth analysis of the geo-social correlations among LBSN users at event level, and proposes a unified influence metric that combines a novel social proximity measure named penalized hitting time, with a geographical weight function modeled by power law distribution.
- HiExpan98
This paper proposes an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy and incorporates a weakly-supervised relation extraction module to extract the initial children of a newly-expanded node.
- DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data95
DPLink, an end-to-end deep learning based framework, is proposed to complete the user identity linkage task for heterogeneous mobility data collected from different services with different properties and it outperforms the state-of-the-art solutions by more than 15% in terms of hit-precision.
- Enhanced Tensor Low-Rank and Sparse Representation Recovery for Incomplete Multi-View Clustering94
A novel Enhanced Tensor Low-rank and Sparse Representation Recovery (ETLSRR) method, which reformulates the IMVC problem as a joint incomplete similarity graphs learning and complete tensor representation recovery problem, to alleviate the negative influence of missing views and data noise.
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- Semantic Scene Completion via Integrating Instances and Scene in-the-Loop83
This work presents a novel framework named Scene-Instance-Scene Network (SISNet), which takes advantages of both in-stance and scene level semantic information, and is capable of inferring fine-grained shape details as well as nearby objects whose semantic categories are easily mixed-up.
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- Discriminative Topic Mining via Category-Name Guided Text Embedding81
CatE is developed, a novel category-name guided text embedding method for discriminative topic mining, which effectively leverages minimal user guidance to learn a discrim inative embedding space and discover category representative terms in an iterative manner.
- Denoising Multi-Source Weak Supervision for Neural Text Classification79
A label denoiser is designed, which estimates the source reliability using a conditional soft attention mechanism and then reduces label noise by aggregating rule-annotated weak labels, which address the rule coverage issue.
- PRED79
A Bayesian non-parametric model, named PRED, to discover periodic mobility patterns by jointly modeling the geographical and temporal information, which outperforms the state-of-the-art methods significantly in four tasks: periodic region discovery, outlier movement finding, period detection, and location prediction.
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- Robust latent low rank representation for subspace clustering75
This work proposes choosing the sparest solution in the solution set when there is noise, and proposes preprocessing the data with robust PCA to demonstrate the advantage of the robust LatLRR over state-of-the-art methods.
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- Learning deep representation for trajectory clustering69
This paper revisits the trajectory clustering problem by learning quality low‐dimensional representations of the trajectories and transforms each trajectory into a feature sequence to describe object movements and employs a sequence‐to‐sequence auto‐encoder to learn fixed‐length deep representations.
- Exploiting and Protecting Dynamic Code Generation68
It is demonstrated that the traditional shellcodeinjection attack can be revived through a code cache injection technique, and a new dynamic code generation architecture is proposed that can defeat the code Cache injection attack with small performance overhead.
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- MFR 2021: Masked Face Recognition Competition54
This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021), which attracted a total of 10 participating teams with valid submissions.
- Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data54
The proposed regularized fine-tuning method outperforms existing calibration methods for text classification in terms of expectation calibration error, misclassification detection, and OOD detection on six datasets.
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- Perception-Guided Multimodal Feature Fusion for Photo Aesthetics Assessment53
A weakly supervised learning paradigm is developed to project the local aesthetic descriptors (graphlets in this work) into a low-dimensional semantic space, and a sparsity-constrained graphlet ranking algorithm is proposed that seamlessly integrates both the low-level and the high-level visual cues.
- RDeepSense52
RDeepSense is proposed, the first deep learning model that provides well-calibrated uncertainty estimations for resource-constrained mobile and embedded devices and can reduce around 90% of the energy consumption and preserve at least the same model accuracy compared with other state-of-the-art methods.
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- I know where you are and what you are sharing: exploiting P2P communications to invade users' privacy50
It is shown how to exploit real-time communication applications to determine the IP address of a targeted user and devise a scheme based on the identification field of the IP datagrams to verify with high accuracy whether the identified user is participating in specific torrents.
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- Scalable music recommendation by search49
This paper presents a search-based solution for scalable music recommendations, in which a music piece is first transformed to a music signature sequence in which each signature characterizes the timbre of a local music clip, using the locality sensitive hashing (LSH).
- GeoBurst+48
A method for effective and real-time local event detection from geo-tagged tweet streams that leverages a novel cross-modal authority measure to identify several pivots in the query window and summarizes the continuous stream, which achieves continuous monitoring of the stream.
- Open-Schema Event Profiling for Massive News Corpora47
This work develops a Bayesian non-parametric model to detect events and event types by exploiting the slot expressions of the entities mentioned in news articles and proposes an unsupervised embedding model for schema induction that encodes the insight.
- Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework46
A novel NER framework is proposed, namely SpanNER, which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data.
- Self-esteem, social support and coping strategies of left-behind children in rural China, and the intermediary role of subjective support:a cross-sectional survey43
Given the significant correlativity between self-esteem, subjective support and coping strategy, it is necessary to promote Chinese LBC’s self- esteem and social support, especially subjective support.
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- Retail supply chain management: a review of theories and practices41
This review focuses on several core components of supply chain management, i.e. vendor management, demand forecasting, inventory management and order fulfillment, and discusses the key innovations from both academia and industry.
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- paper2repo: GitHub Repository Recommendation for Academic Papers37
This work describes a novel item-item cross-platform recommender system, paper2repo, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic, and is believed to be the first distant-supervised cross- platform (paper to repository) matching system.
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- LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values32
This paper proposes Logistic PARAFAC2 (LogPar) by modeling the binary irregular Tensor with Bernoulli distribution parameterized by an underlying real-valued tensor and approximate the underlying tensor with a positive-unlabeled learning loss function to account for the missing values.
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- Perceptually Aware Image Retargeting for Mobile Devices26
This paper proposes a novel retargeting framework that rapidly shrinks a photograph/video by leveraging human gaze behavior, and derives a geometry-preserving graph ranking algorithm that efficiently selects a few salient object patches to mimic the human gaze shifting path (GSP) when viewing a scene.
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- Visual aesthetic understanding: Sample-specific aesthetic classification and deep activation map visualization20
An end-to-end convolutional neural network model which simultaneously implements aesthetic classification and understanding is proposed and it is found that dropping out ambiguous image is a special case of the sample-specific method, and also figure out that as the weights of the non-ambiguous images increase, the performance is positively affected.
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- Spatiotemporal Activity Modeling Under Data Scarcity: A Graph-Regularized Cross-Modal Embedding Approach15
BranchNet, a spatiotemporal activity model that transfers knowledge from external sources for alleviating data scarcity, is proposed, which adopts a graph-regularized cross-modal embedding framework and is capable of effectively transferring knowledge from External sources to learn better spatiotmporal activity models and outperforming strong baseline methods.
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- Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR.13
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- Toward Personalized Activity Level Prediction in Community Question Answering Websites13
An algorithm termed PALP is the first approach to address a personalized activity level prediction model for CQA websites and takes into consideration user behavior change over time and focuses specifically on expert users.
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- Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method10
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- Time‐Delayed Feedback Tristable Stochastic Resonance Weak Fault Diagnosis Method and Its Application8
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- Evaluation of Test Methods for Triaxially Braided Composites Using a Meso-scale Finite Element Model1
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- Error Bounds for Real Function Classes Based on Discretized Vapnik-Chervonenkis Dimensions.1
This paper proposes the discretized VC dimension obtained by discretizing the range of a real function class, and points out that Sauer\'s Lemma is valid for this dimension.
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- A StyleGAN3-Based Style Transfer Creation Assistance System for Digital Media Art Teaching Workshops–
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- Fractional Sparse Representation Based Classification and Multi-view Extension for Face Recognition.–
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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