PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
Performance of the CMS Level-1 trigger in proton-proton collisions at√s= 13TeV
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Presents a reusable open-source framework for mapping quantized transformer layers to AMD Versal AI Engine tiles for jet tagging at the LHC.
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.
citing papers explorer
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Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
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Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines
Presents a reusable open-source framework for mapping quantized transformer layers to AMD Versal AI Engine tiles for jet tagging at the LHC.
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Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.