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How Do Deep-Learning Framework Versions Affect the Reproducibility of Neural Network Models?
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jama.2019.20866openalex-snapshot
Challenges to the Reproducibility of Machine Learning Models in Health Care
inferredcites →https://doi.org/10.1002/nme.6593openalex-snapshot
Error control and loss functions for the deep learning inversion of borehole resistivity measurements
inferredcites →https://doi.org/10.1007/978-3-319-61657-5openalex-snapshot
Deep Learning for Biometrics
inferredcites →https://doi.org/10.1007/978-3-662-44874-8openalex-snapshot
Introduction to Evolutionary Computing
inferredcites →https://doi.org/10.1007/s10664-023-10291-1openalex-snapshot
Bugs in machine learning-based systems: a faultload benchmark
inferredcites →https://doi.org/10.1016/j.future.2021.06.014openalex-snapshot
Do machine learning platforms provide out-of-the-box reproducibility?
inferredcites →https://doi.org/10.1016/j.jss.2021.110935openalex-snapshot
The symptoms, causes, and repairs of bugs inside a deep learning library
inferredcites →https://doi.org/10.1016/j.jss.2021.111031openalex-snapshot
A software engineering perspective on engineering machine learning systems: State of the art and challenges
inferredcites →https://doi.org/10.1016/j.patter.2020.100073openalex-snapshot
dtoolAI: Reproducibility for Deep Learning
inferredcites →https://doi.org/10.1038/s41586-020-2766-yopenalex-snapshot
Transparency and reproducibility in artificial intelligence
inferredcites →https://doi.org/10.1073/pnas.0709640104openalex-snapshot
Randomized algorithms for the low-rank approximation of matrices
inferredcites →https://doi.org/10.1109/access.2020.3039833openalex-snapshot
Challenges for the Repeatability of Deep Learning Models
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01333openalex-snapshot
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective
inferredcites →https://doi.org/10.1109/icse-seip.2019.00042openalex-snapshot
Software Engineering for Machine Learning: A Case Study
inferredcites →https://doi.org/10.1109/qrs54544.2021.00116openalex-snapshot
The Challenge of Reproducible ML: An Empirical Study on The Impact of Bugs
inferredcites →https://doi.org/10.1145/3213846.3213858openalex-snapshot
Identifying implementation bugs in machine learning based image classifiers using metamorphic testing
inferredcites →https://doi.org/10.1145/3213846.3213866openalex-snapshot
An empirical study on TensorFlow program bugs
inferredcites →https://doi.org/10.1145/3297280.3297411openalex-snapshot
How do implementation bugs affect the results of machine learning algorithms?
inferredcites →https://doi.org/10.1145/3324884.3416545openalex-snapshot
Problems and opportunities in training deep learning software systems
inferredcites →https://doi.org/10.1145/3338906.3338955openalex-snapshot
A comprehensive study on deep learning bug characteristics
inferredcites →https://doi.org/10.1145/3377811.3380395openalex-snapshot
Taxonomy of real faults in deep learning systems
inferredcites →https://doi.org/10.1162/tacl_a_00018openalex-snapshot
Questionable Answers in Question Answering Research: Reproducibility and Variability of Published Results
inferredcites →https://doi.org/10.1190/geo2021-0240.1openalex-snapshot
A deep learning approach to design a borehole instrument for geosteering
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Soft Computing Techniques in Engineering, Health, Mathematical and Social Sciences
inferredcites →https://doi.org/10.1609/aaai.v32i1.11503openalex-snapshot
State of the Art: Reproducibility in Artificial Intelligence
inferredcites →https://doi.org/10.1609/aimag.v39i3.2816openalex-snapshot
On Reproducible AI: Towards Reproducible Research, Open Science, and Digital Scholarship in AI Publications
inferredcites →https://doi.org/10.1613/jair.953openalex-snapshot
SMOTE: Synthetic Minority Over-sampling Technique
inferredcites →https://doi.org/10.3390/make3010004openalex-snapshot
AI System Engineering—Key Challenges and Lessons Learned
inferredcites →https://doi.org/10.48550/arxiv.1912.01703openalex-snapshot
PyTorch: An Imperative Style, High-Performance Deep Learning Library
inferredcites →https://doi.org/10.48550/arxiv.2106.11872openalex-snapshot
Randomness In Neural Network Training: Characterizing The Impact of Tooling
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/aaai.70002openalex-snapshot
Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers
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inferred← citeshttps://doi.org/10.1016/j.jpet.2026.104986openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.jss.2026.112999openalex-snapshot
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inferred← citeshttps://doi.org/10.1038/s44320-024-00070-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1134/s2070048224020169openalex-snapshot
Neural Network Model for Estimating the Quality Indicators of Industrial Fractionator Products
inferred← citeshttps://doi.org/10.1145/3820002.3828592openalex-snapshot
The AI Reproducibility Index: Ranking Venues, Countries and Institutions by Reproducibility
inferred← citeshttps://doi.org/10.1177/16094069261459023openalex-snapshot
Acceleration and Recursion in AI-Mediated Qualitative Research: Discussing Adapted Criteria for Qualitative Inquiry in the Techno-Epistemic Age
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Нейросетевая модель для оценки показателей качества продуктов промышленного фракционатора
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Container overhead or stale dependencies? Decomposing the latency and energy cost of containerised inference on an edge node
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Empirical Review of Traditional and Recent Balancing Techniques for Image-Based Physical Violence Classification Across Diverse Imbalance Scenarios and Multiple Datasets
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Bijective Network-to-Image Encoding for Interpretable CNN-Based Intrusion Detection System
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