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The nondeterminism of Deep Learning (DL) training algorithms and its influence on the explainability of neural network (NN) models are investigated in this work with the help of image classification examples.
Explaining and Harnessing Adversarial Examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Deep Residual Learning for Image Recognition
K. He et al · 2016
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Rethinking the Inception Architecture for Computer Vision
C. Szegedy et al · 2016
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What does fault tolerant deep learning need from MPI?
V. Amatya et al · 2017
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Xception: Deep Learning with Depthwise Separable Convolutions
F. Chollet · 2017
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
R. R. Selvaraju et al · 2017
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Axiomatic Attribution for Deep Networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
C. Szegedy et al · 2017
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State of the Art: Reproducibility in Artificial Intelligence
O. E. Gundersen and S. Kjensmo · 2018
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Artificial intelligence faces reproducibility crisis
M. Hutson · 2018
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Missing data hinder replication of artificial intelligence studies, 2018
M. Hutson · 2018
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Challenges to the Reproducibility of Machine Learning Models in Health Care
A. L. Beam, A. K. Manrai, and M. Ghassemi · 2019
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Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey
G. Nguyen et al · 2019
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ICLR Reproducibility Challenge 2019
J. Pineau et al · 2019
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Investigating the Performance of Machine Learning Algorithms for Improving Fault Tolerance for Large Scale Workflow Applications in Cloud Computing
S. Prathibha · 2019
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Energy and Policy Considerations for Deep Learning in NLP
E. Strubell, A. Ganesh, and A. McCallum · 2019
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Problems and Opportunities in Training Deep Learning Software Systems: An Analysis of Variance
H. V. Pham et al · 2020
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J. Pineau et al · 2020
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NeurIPS 2019 Reproducibility Challenge
K. Sinha et al · 2020
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How to Solve ML’s Reproducibility Crisis in 3 Easy Steps, 2021
A. Abid · 2021
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Ensembles: the only (almost) free Lunch in Machine Learning, 2020
D. Borisenko · 2021
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AI Research, Replicability and Incentives, 2020
D. Britz · 2021
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Reproducibility standards for machine learning in the life sciences
B. J. Heil et al · 2021
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Reproducible Machine Learning, 2020
P. Hemant · 2021
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The reproducibility crisis in Machine Learning, 2020
M. Hobbhahn · 2021
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Deep Reinforcement Learning Doesn’t Work Yet, 2018
A. Irpan · 2021
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Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control, 2017
R. Islam et al · 2021
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Understanding Deep Learning Models with Integrated Gradients, 2020
R. Khandelwal · 2021
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