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- Ivor Wai-Hung TsangSuggested from co-authorship
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Works21 from public data
- Survey on Multi-Output Learning274
The four Vs of multi-output learning are characterized, i.e., volume, velocity, variety, and veracity, and the ways in which the four Vs both benefit and bring challenges to multi- output learning by taking inspiration from big data are examined.
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- A review of time series forecasting and spatio-temporal series forecasting in deep learning33
This paper provides a comprehensive review of recent deep learning models for time series and spatio-temporal forecasting, with a focus on representative approaches based on Transformer architectures and hybrid designs.
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- Anti-Honeypot Enabled Optimal Attack Strategy for Industrial Cyber-Physical Systems14
This paper presents an anti-honeypot enabled optimal attack strategy for ICPS, by employing a novel game-theoretical approach that offers the attackers an optimal tactic to compromise the target ICPS protected by honeypots, while having only incomplete knowledge of the defensive mechanisms.
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- Label Embedding with Partial Heterogeneous Contexts9
A general Partial Heterogeneous Context Label Embedding (PHCLE) framework, which overcomes the partial context problem and can nicely incorporate more contexts, which both cannot be tackled with existing multi-context label embedding methods.
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- Multiview Alignment and Generation in CCA via Consistent Latent Encoding4
This letter presents adversarial CCA (ACCA), which achieves consistent latent encodings by matching the marginalization of the joint distribution of multiview random variables under different forms of factorization, and reveals that ACCA is flexible for handling implicit distributions.
- Multi-Context Label Embedding.2
This paper proposes a Multi-Context Label Embedding (MCLE) approach to incorporate multiple label contexts, e.g., label hierarchy and attributes, within a unified matrix factorization framework and imposes sparsity constraint on the multi-context framework to strengthen the interpretability of the learned label embedding.
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- 3D Texture Recognition for RGB-D Images1
A novel 3D object recognition system that captures both the color and depth information of 3D objects using Kinect, and represents them in RGB-D images, and exploits metric learning methods for the K-nearest neighbor KNN classifier.
- Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence–
This paper proposes a novel and general framework, called relational Fisher analysis (RFA), which successfully integrates relational information into the dimensionality reduction model, and adopts the kernel trick to RFA and proposes the kernelized RFA (KRFA).
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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 2026-10-11. 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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