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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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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 ...
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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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...
Abstract page for arXiv paper 2504.02778: Multi-Head Adaptive Graph Convolution Network for S... this https URLSubjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs...
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...
View PDFAbstract:We study how multi-head softmax attention models are trained to perform in-co... arXiv-issued DOI via DataCite Submission history Access Paper: View a PDF of the paper titled In...