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Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance.
Xv.—the correlation between relatives on the supposition of mendelian inheritance
Ronald A Fisher · 1919
Earlier work this paper cites.
An approximate distribution of estimates of variance components
Franklin E Satterthwaite · 1946
Earlier work this paper cites.
Small sample inference for fixed effects from restricted maximum likelihood
Michael G Kenward and James H Roger · 1997
Earlier work this paper cites.
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
Makoto Matsumoto and Takuji Nishimura · 1998
Earlier work this paper cites.
Linear mixed models and penalized least squares
Douglas M Bates and Saikat DebRoy · 2004
Earlier work this paper cites.
One-shot learning of object categories
Li Fei-fei, Rob Fergus, and Pietro Perona · 2006
Earlier work this paper cites.
Data analysis using regression and multilevel/hierarchical models
Andrew Gelman and Jennifer Hill · 2006
Earlier work this paper cites.
Fitting linear mixed-effects models using lme4
Douglas Bates, Martin Mächler, Ben Bolker, and Steve Walker · 2014
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, and Daan Wierstra · 2016
Cited alongside, same era.
lmertest package: tests in linear mixed effects models
Alexandra Kuznetsova, Per B Brockhoff, and Rune Haubo Bojesen Christensen · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
Reporting score distributions makes a difference: Performance study of lstm-networks for sequence tagging
Nils Reimers and Iryna Gurevych · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Open is not enough
Xiaoli Chen, Sünje Dallmeier-Tiessen, Robin Dasler, Sebastian Feger, Pamfilos Fokianos, Jose Benito Gonzalez, Harri Hirvonsalo, Dinos Kousidis, Artemis Lavasa, Salvatore Mele, et al · 2018
Later among the works it cites.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Troubling trends in machine learning scholarship
Zachary C Lipton and Jacob Steinhardt · 2018
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On the state of the art of evaluation in neural language models
Gábor Melis, Chris Dyer, and Phil Blunsom · 2018
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Tadam: Task dependent adaptive metric for improved few-shot learning
Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste · 2018
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Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Building a reproducible machine learning pipeline
Małgorzata Cebrat and Florian Hartl · 2018
Cited alongside, same era.
A practical taxonomy of reproducibility for machine learning research
Rachael Tatman · 2018
Later among the works it cites.