FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Nguyen, Javier Duarte, and Zhenbin Wu
3 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
AI Engines enable larger low-latency neural networks for extreme-edge scientific computing on FPGAs than programmable logic, via a new latency-adjusted resource equivalence metric and tailored optimizations.
A distributed arithmetic algorithm for CMVM operations on FPGAs reduces area by up to one third and latency for quantized neural networks, integrated into hls4ml.
citing papers explorer
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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Design Rules for Extreme-Edge Scientific Computing on AI Engines
AI Engines enable larger low-latency neural networks for extreme-edge scientific computing on FPGAs than programmable logic, via a new latency-adjusted resource equivalence metric and tailored optimizations.
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da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
A distributed arithmetic algorithm for CMVM operations on FPGAs reduces area by up to one third and latency for quantized neural networks, integrated into hls4ml.