Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments

10 April 2022

Papers citing "Reducing Model Jitter: Stable Re-training of Semantic Parsers in Production Environments"

6 / 6 papers shown

Title
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation Branislav Pecher Ján Cegin Róbert Belanec Jakub Simko Ivan Srba M. Bieliková 37 1 0 18 Jun 2024
FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning Sejoon Oh Berk Ustun Julian McAuley Srijan Kumar 19 1 0 05 Feb 2024
Backward Compatibility During Data Updates by Weight Interpolation Raphael Schumann Elman Mansimov Yi-An Lai Nikolaos Pappas Xibin Gao Yi Zhang 11 4 0 25 Jan 2023
Anti-Distillation: Improving reproducibility of deep networks G. Shamir Lorenzo Coviello 34 20 0 19 Oct 2020
Large scale distributed neural network training through online distillation Rohan Anil Gabriel Pereyra Alexandre Passos Róbert Ormándi George E. Dahl Geoffrey E. Hinton FedML 267 404 0 09 Apr 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016