Using J-K fold Cross Validation to Reduce Variance When Tuning NLP
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Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models

19 June 2018

David S. Leslie

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Papers citing "Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models"

9 / 9 papers shown

Title
Distributed Representations of Atoms and Materials for Machine Learning Luis M. Antunes R. Grau‐Crespo K. Butler AI4CE 32 27 0 30 Jul 2021
GIBBON: General-purpose Information-Based Bayesian OptimisatioN Henry B. Moss David S. Leslie Javier I. González Paul Rayson 90 44 0 05 Feb 2021
BOSH: Bayesian Optimization by Sampling Hierarchically Henry B. Moss David S. Leslie Paul Rayson 66 8 0 02 Jul 2020
MUMBO: MUlti-task Max-value Bayesian Optimization Henry B. Moss David S. Leslie Paul Rayson 88 34 0 22 Jun 2020
Do sequence-to-sequence VAEs learn global features of sentences? Tom Bosc Pascal Vincent SSL 56 9 0 16 Apr 2020
Hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and "transfer learning" technique A. Demyanchuk E. Pushkina N. Russkikh D. Shtokalo S. Mishinov MedIm 107 2 0 23 Sep 2019
FIESTA: Fast IdEntification of State-of-The-Art models using adaptive bandit algorithms Henry B. Moss Andrew Moore David S. Leslie Paul Rayson 51 5 0 28 Jun 2019
Temporal and Aspectual Entailment Thomas Kober Sander Bijl de Vroe Mark Steedman 59 16 0 02 Apr 2019
In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse Mahmoud El-Haj Paul Rayson M. Walker S. Young Vasiliki Simaki 36 96 0 28 Mar 2019

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