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Multi-typed Objects Multi-view Multi-instance Multi-label Learning
Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L)...
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Deep Incomplete Multi-View Multiple Clusterings
Multi-view clustering aims at exploiting information from multiple heter...
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Partial Multi-label Learning with Label and Feature Collaboration
Partial multi-label learning (PML) models the scenario where each traini...
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Attention-Aware Answers of the Crowd
Crowdsourcing is a relatively economic and efficient solution to collect...
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Multi-View Multiple Clusterings using Deep Matrix Factorization
Multi-view clustering aims at integrating complementary information from...
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Prototypical Networks for Multi-Label Learning
We propose to address multi-label learning by jointly estimating the dis...
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Active Multi-Label Crowd Consensus
Crowdsourcing is an economic and efficient strategy aimed at collecting ...
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Cross-modal Zero-shot Hashing
Hashing has been widely studied for big data retrieval due to its low st...
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Weakly-paired Cross-Modal Hashing
Hashing has been widely adopted for large-scale data retrieval in many d...
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ActiveHNE: Active Heterogeneous Network Embedding
Heterogeneous network embedding (HNE) is a challenging task due to the d...
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Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization
Multi-view Multi-instance Multi-label Learning(M3L) deals with complex o...
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Multi-View Multiple Clustering
Multiple clustering aims at exploring alternative clusterings to organiz...
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Ranking-based Deep Cross-modal Hashing
Cross-modal hashing has been receiving increasing interests for its low ...
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Multiple Independent Subspace Clusterings
Multiple clustering aims at discovering diverse ways of organizing data ...
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