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at each step. While Multi-Head Latent Attention (MLA) significantly reduces the total KV cache size, it suffers from a sharding bottleneck during distributed decoding via Tensor Parallelism (TP). Since...
where the most informative frames are first selected through a multi-head voting (MHV) module. These selected frames are then refined via a cross-layer refinement (CLR) module to enhance the mod...
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Abstract page for arXiv paper 2508.04251: T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion Computer Science Machine Learning arXiv:2508.042...
Abstract page for arXiv paper 2505.17344: A Multi-Head Attention Soft Random Forest for Interpre... arXiv-issued DOI via DataCite (pending registration) Submission history Access Paper: View a PD...
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we propose a novel multi-head decoder architecture that exploits the inherent redundancy in point clouds by reconstructing complete shapes from multiple independent heads, each operating on a dist...
multi-head self-attention is then constructed to capture global dependencies and aggregate features from multiple perspectives, thereby enhancing the ability to detect abnormal patterns. The network ...
a straightforward entropy-guided approach that composes multi-head rewards by downweighting rules exhibiting high rating entropy. Theoretically, we demonstrate that rules with elevated entropy natu...
Abstract page for arXiv paper 2502.17987: MAGE: Multi-Head Attention Guided Embeddings for L... arXiv-issued DOI via DataCite (pending registration) Submission history Access Paper: View a PD...
Title:Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Tran... arXiv-issued DOI via DataCite (pending registration) Submission history Access Paper: View a PD...