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Sources of Irreproducibility in Machine Learning: A Review

Sources of Irreproducibility in Machine Learning: A Review

15 April 2022
Odd Erik Gundersen
Kevin Coakley
Christine R. Kirkpatrick
Yolanda Gil
    SyDa
ArXivPDFHTML

Papers citing "Sources of Irreproducibility in Machine Learning: A Review"

11 / 11 papers shown
Title
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction
Farhad Pourkamali-Anaraki
36
0
0
24 Apr 2025
EBES: Easy Benchmarking for Event Sequences
EBES: Easy Benchmarking for Event Sequences
Dmitry Osin
Igor Udovichenko
Viktor Moskvoretskii
Egor Shvetsov
Evgeny Burnaev
48
1
0
04 Oct 2024
Weak baselines and reporting biases lead to overoptimism in machine
  learning for fluid-related partial differential equations
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
N. McGreivy
Ammar Hakim
AI4CE
34
42
0
09 Jul 2024
Generalizability of experimental studies
Generalizability of experimental studies
Federico Matteucci
Vadim Arzamasov
Jose Cribeiro-Ramallo
Marco Heyden
Konstantin Ntounas
Klemens Bohm
47
0
0
25 Jun 2024
Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers
Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers
Harald Semmelrock
Tony Ross-Hellauer
Simone Kopeinik
Dieter Theiler
Armin Haberl
Stefan Thalmann
Dominik Kowald
65
6
0
20 Jun 2024
A Second Look on BASS -- Boosting Abstractive Summarization with Unified
  Semantic Graphs -- A Replication Study
A Second Look on BASS -- Boosting Abstractive Summarization with Unified Semantic Graphs -- A Replication Study
Osman Alperen Koras
Jorg Schlotterer
Christin Seifert
34
1
0
05 Mar 2024
Examining the Effect of Implementation Factors on Deep Learning
  Reproducibility
Examining the Effect of Implementation Factors on Deep Learning Reproducibility
Kevin Coakley
Christine R. Kirkpatrick
Odd Erik Gundersen
15
2
0
11 Dec 2023
A Two-Sided Discussion of Preregistration of NLP Research
A Two-Sided Discussion of Preregistration of NLP Research
Anders Søgaard
Daniel Hershcovich
Miryam de Lhoneux
OnRL
AI4CE
33
3
0
20 Feb 2023
A Rigorous Uncertainty-Aware Quantification Framework Is Essential for
  Reproducible and Replicable Machine Learning Workflows
A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows
Line C. Pouchard
Kristofer G. Reyes
Francis J. Alexander
Byung-Jun Yoon
27
2
0
13 Jan 2023
Efficient Methods for Natural Language Processing: A Survey
Efficient Methods for Natural Language Processing: A Survey
Marcos Vinícius Treviso
Ji-Ung Lee
Tianchu Ji
Betty van Aken
Qingqing Cao
...
Emma Strubell
Niranjan Balasubramanian
Leon Derczynski
Iryna Gurevych
Roy Schwartz
28
109
0
31 Aug 2022
With Little Power Comes Great Responsibility
With Little Power Comes Great Responsibility
Dallas Card
Peter Henderson
Urvashi Khandelwal
Robin Jia
Kyle Mahowald
Dan Jurafsky
230
115
0
13 Oct 2020
1