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.
Fast inference of deep neural networks in FPGAs for particle physics
7 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
Differential halo zonotopes enable static verification of global robustness in DNNs by jointly propagating pairs of perturbed inputs while bounding divergence, with a relaxed confidence-based variant.
FPGA implementations for full matrix-element workflow on e+e- to mu+mu- and color-algebra kernels on gg to ttbar+X achieve speedups and energy gains over CPU/GPU while preserving numerical accuracy.
SNAC-Pack is a new framework for hardware-aware neural architecture codesign that uses surrogate models, NSGA-II search, quantization-aware training, and hls4ml synthesis to produce compact FPGA-deployable models.
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.
WaveDriver is a laser guide star AO concept whose initial simulations indicate it may be required to meet HWO primary mirror segment stability and low-order wavefront stability requirements.
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
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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FPGA Acceleration of Matrix-Element Calculations for Monte Carlo Event Generation
FPGA implementations for full matrix-element workflow on e+e- to mu+mu- and color-algebra kernels on gg to ttbar+X achieve speedups and energy gains over CPU/GPU while preserving numerical accuracy.