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Deep Multi-View Clustering via Multiple Embedding We demonstrate the effectiveness of our approach on several challenging image datasets, where sig-nificant superiority can be found over single multi-view base-lines and the state-of-the-art multi-view clustering methods
Auto-attention mechanism for multi-view deep embedding clustering . . . Incorporating a triple fusion technique, this research suggests an innovative multi-view deep embedding clustering (MDEC) model The suggested model can jointly acquire the specific knowledge in each view as well as the information fragment of the collective views
Deep Fair Multi-View Clustering with Attention KAN In conclusion, our proposed fair multi-view clustering method utilizing the Kolmogorov-Arnold network (KAN) attention mechanism effectively addresses the limitations of existing approaches that enforce uniform distribution of sensitive attributes within clusters
A Gentle Introduction to Multi-Head Latent Attention (MLA) Low-Rank Approximation of Matrices Multi-Head Attention (MHA) and Grouped-Query Attention (GQA) are the attention mechanisms used in almost all transformer models Recently, a new attention mechanism called Multi-head Latent Attention (MLA) was proposed in DeepSeek-V2 to further reduce computational cost and speed up inference