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- Graph Representation Learning via Graphical Mutual Information Maximization706
An unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder is developed, which outperforms state-of-the-art unsuper supervised counterparts, and even sometimes exceeds the performance of supervised ones.
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- ANOMALOUS: A Joint Modeling Approach for Anomaly Detection on Attributed Networks264
This paper proposes a novel joint framework to conduct attribute selection and anomaly detection as a whole based on CUR decomposition and residual analysis and results on both synthetic and real-world datasets corroborate the effectiveness of the proposed framework.
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- Cross-person activity recognition using reduced kernel extreme learning machine173
Experimental results show that, the proposed TransRKELM model can adapt the classifier to new device users quickly and obtain good recognition performance.
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- Virtual machine consolidated placement based on multi-objective biogeography-based optimization154
This work is the first approach that applies biogeography-based optimization (BBO) to virtual machine placement and it is shown that VMPMBBO has better convergence characteristics and is more computationally efficient as well as robust.
- An optimized approach for storing and accessing small files on cloud storage151
Experimental results demonstrate that the proposed schemes effectively improve the storage and access efficiencies of small files, compared with native HDFS and a Hadoop file archiving facility.
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- BlueSky Cloud Framework: An E-Learning Framework Embracing Cloud Computing129
In this paper, an E-Learning framework based on cloud computing is presented, namely BlueSky cloud framework, which solves the challenges faced by E-learning, and improves the performance, availability and scalability of E- learning systems.
- A multi-constraint learning path recommendation algorithm based on knowledge map127
A new multi-constraint learning path recommendation algorithm based on knowledge map is proposed, in which the variables and their weighted coefficients considers different learning path preferences of the learners in different learning scenarios as well as learning resource organization and fragmented time.
- Self-weighted Robust LDA for Multiclass Classification with Edge Classes123
A novel self-weighted robust LDA with ℓ2,1-norm based pairwise between-class distance criterion, called SWRLDA, is proposed, which can automatically avoid the optimal mean calculation and simultaneously learn adaptive weights for each class pair without setting any additional parameter.
- Semantics-Guided Contrastive Network for Zero-Shot Object Detection114
A novel Semantics-Guided Contrastive Network for ZSD is developed, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection and outperforms the previous state-of-the-art on both ZSD and generalized ZSD tasks.
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- Automatic Generation of Medical Imaging Diagnostic Report with Hierarchical Recurrent Neural Network114
- Cross-media analysis and reasoning: advances and directions104
By presenting approaches, advances, and future directions in cross-media analysis and reasoning, the goal is to draw more attention to the state-of-the-art advances in the field, and to provide technical insights by discussing the challenges and research directions in these areas.
- Scalable attack on graph data by injecting vicious nodes103
This paper develops a more scalable framework named Approximate Fast Gradient Sign Method which considers a more practical attack scenario where adversaries can only inject new vicious nodes to the graph while having no control over the original graph.
- Research on Extreme Learning of Neural Networks93
A novel algorithm called Regularized Extreme Learning Machine (RELM) based on structural risk minimization and weighted least square is proposed in this paper, which has better generalization, robustness and controllability than the original ELM.
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- Ordinal extreme learning machine90
The proposed encoding-based framework for ordinal regression which includes three encoding schemes: single multi-output classifier, multiple binary-classifications with one-against-all (OAA) decomposition method and one- against-one (OAO) method, and the SLFN was redesigned for ordinals regression problems based on the proposed framework.
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 2026-10-14. 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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