The Jackknife

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Uncertainty-Aware Reinforcement Learning for Collision Avoidance (2017, methodology): “Bootstrapping: Bootstrapping [5, 6] is a simple and effective method of estimating model uncertainty using resampling that can be used with any discriminatively trained model.”
A Race-Track Trapped-Ion Quantum Processor (2023, methodology): “All error bars are calculated using Jack-knife resampling [92], except for those where the sampling number was too low to calculate an error (i.”
Exploring Students’ Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model (2019, methodology): “The model was made to run through a bootstrap re-sampling routine to obtain the path significances [108].”
Realization of Real-Time Fault-Tolerant Quantum Error Correction (2021, methodology): “To better estimate the error bars due experimental noise fluctuations, we used a Jackknife resampling method [63] to estimate the aver0 1 2 3 4 5 6 QEC cycles 10 3 10 2 10 1”