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What Is the Intended Usage Context of This Model? An Exploratory Study of Pre-Trained Models on Various Model Repositories

https://doi.org/10.1145/3569934
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Microsoft COCO: Common Objects in Context
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DLBench: a comprehensive experimental evaluation of deep learning frameworks
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Pre-trained models for natural language processing: A survey
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Learnware: on the future of machine learning
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On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning
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Augmenting commit classification by using fine-grained source code changes and a pre-trained deep neural language model
inferredcites →https://doi.org/10.1016/j.jss.2020.110653openalex-snapshot
Software reuse cuts both ways: An empirical analysis of its relationship with security vulnerabilities
inferredcites →https://doi.org/10.1016/j.jss.2021.110936openalex-snapshot
Code smell detection by deep direct-learning and transfer-learning
inferredcites →https://doi.org/10.1016/j.knosys.2021.107476openalex-snapshot
How well do pre-trained contextual language representations recommend labels for GitHub issues?
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Simulation model reuse: definitions, benefits and obstacles
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Answering the Call for a Standard Reliability Measure for Coding Data
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Design by contract: the lessons of Ariane
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Proceedings. 26th International Conference on Software Engineering
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Proceedings 2021 IEEE International Conference on Software Maintenance and Evolution
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Software Engineering for Machine-Learning Applications: The Road Ahead
inferredcites →https://doi.org/10.1109/ms.2019.2934192openalex-snapshot
Better Code Reviews With Design by Contract
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A Comparative Measurement Study of Deep Learning as a Service Framework
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Software reuse research: status and future
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Mining Fix Patterns for FindBugs Violations
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Smart Contract Development: Challenges and Opportunities
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A Qualitative Study of the Benefits and Costs of Logging From Developers’ Perspectives
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Making the most of small Software Engineering datasets with modern machine learning
inferredcites →https://doi.org/10.1109/tse.2022.3152148openalex-snapshot
An Empirical Study of Yanked Releases in the Rust Package Registry
inferredcites →https://doi.org/10.1109/tse.2022.3178469openalex-snapshot
On the Validity of Pre-Trained Transformers for Natural Language Processing in the Software Engineering Domain
inferredcites →https://doi.org/10.1142/s0218194021400155openalex-snapshot
Patterns for Reuse in Production Systems Engineering
inferredcites →https://doi.org/10.1145/3106237.3106290openalex-snapshot
Are deep neural networks the best choice for modeling source code?
inferredcites →https://doi.org/10.1145/3196398.3196448openalex-snapshot
Word embeddings for the software engineering domain
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Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement
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Measuring and Mitigating Unintended Bias in Text Classification
inferredcites →https://doi.org/10.1145/3287560.3287596openalex-snapshot
Model Cards for Model Reporting
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Evaluating representation learning of code changes for predicting patch correctness in program repair
inferredcites →https://doi.org/10.1145/3468264openalex-snapshot
Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
inferredcites →https://doi.org/10.1145/3485135openalex-snapshot
What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning
inferredcites →https://doi.org/10.1145/3485275openalex-snapshot
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
inferredcites →https://doi.org/10.1145/3487043openalex-snapshot
Software Engineering for AI-Based Systems: A Survey
inferredcites →https://doi.org/10.1145/3510003openalex-snapshot
Proceedings of the 44th International Conference on Software Engineering
inferredcites →https://doi.org/10.1145/3511616.3513104openalex-snapshot
A Novel Policy for Pre-trained Deep Reinforcement Learning for Speech Emotion Recognition
inferredcites →https://doi.org/10.1147/jrd.2019.2942288openalex-snapshot
FactSheets: Increasing trust in AI services through supplier's declarations of conformity
inferredcites →https://doi.org/10.1162/coli.07-034-r2openalex-snapshot
Inter-Coder Agreement for Computational Linguistics
inferredcites →https://doi.org/10.1186/s40537-020-00335-4openalex-snapshot
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inferredcites →https://doi.org/10.1201/9781315371658openalex-snapshot
Machine Learning: Algorithms and Applications
inferredcites →https://doi.org/10.1360/n112017-00106openalex-snapshot
Model reuse with domain knowledge
inferredcites →https://doi.org/10.1638/2019-0219openalex-snapshot
EVALUATION OF THE INTER- AND INTRAINDIVIDUAL AGREEMENT OF A PODODERMATITIS SCORING MODEL IN GREATER FLAMINGOS (PHOENICOPTERUS ROSEUS)
inferredcites →https://doi.org/10.18653/v1/2020.acl-mainopenalex-snapshot
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
inferredcites →https://doi.org/10.18653/v1/2021.emnlp-main.685openalex-snapshot
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation
inferredcites →https://doi.org/10.18653/v1/2021.findings-emnlpopenalex-snapshot
Findings of the Association for Computational Linguistics: EMNLP 2021
inferredcites →https://doi.org/10.18653/v1/w18-5446openalex-snapshot
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
inferredcites →https://doi.org/10.21437/interspeech.2022-143openalex-snapshot
XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale
inferredcites →https://doi.org/10.3390/app11020796openalex-snapshot
Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-snapshot
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1705.06950openalex-snapshot
The Kinetics Human Action Video Dataset
inferredcites →https://doi.org/10.48550/arxiv.1802.01096openalex-snapshot
Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks
inferredcites →https://doi.org/10.48550/arxiv.2006.12641openalex-snapshot
Exploring Software Naturalness through Neural Language Models
inferredcites →https://doi.org/10.48550/arxiv.2006.16831openalex-snapshot
SE3M: A Model for Software Effort Estimation Using Pre-trained Embedding Models
inferredcites →https://doi.org/10.48550/arxiv.2009.06520openalex-snapshot
A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research
inferredcites →https://doi.org/10.48550/arxiv.2102.10535openalex-snapshot
Automatic Code Generation using Pre-Trained Language Models
inferredcites →https://doi.org/10.48550/arxiv.2104.11178openalex-snapshot
VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
inferredcites →https://doi.org/10.48550/arxiv.2202.08701openalex-snapshot
Revisiting reopened bugs in open source software systems
inferredcites →https://doi.org/10.57702/jtuxnt5mopenalex-snapshot
The kinetics human action video dataset

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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How to Select Pre-Trained Code Models for Reuse? A Learning Perspective
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Generative Information Systems Are Great If You Can Read
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Analyzing the Evolution and Maintenance of ML Models on Hugging Face
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PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software
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Automated categorization of pre-trained models in software engineering: A case study with a Hugging Face dataset
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What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims
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Large Language Models for Software Engineering: A Systematic Literature Review
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A First Look at Model Supply Chain: From the Risk Perspective
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Enhancing Software Effort Estimation with Pre-Trained Word Embeddings: A Small-Dataset Solution for Accurate Story Point Prediction
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