How do Machine Learning Models Change?
Fuente:
arXiv
Guardado en:
| Autores principales: | Castaño, Joel, Cabañas, Rafael, Salmerón, Antonio, Lo, David, Martínez-Fernández, Silverio |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Lessons Learned from Mining the Hugging Face Repository
por: Castaño, Joel, et al.
Publicado: (2024)
por: Castaño, Joel, et al.
Publicado: (2024)
Impact of ML Optimization Tactics on Greener Pre-Trained ML Models
por: Álvarez, Alexandra González, et al.
Publicado: (2024)
por: Álvarez, Alexandra González, et al.
Publicado: (2024)
Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings
por: González, Alexandra, et al.
Publicado: (2025)
por: González, Alexandra, et al.
Publicado: (2025)
Analyzing the Evolution and Maintenance of ML Models on Hugging Face
por: Castaño, Joel, et al.
Publicado: (2023)
por: Castaño, Joel, et al.
Publicado: (2023)
Towards a Classification of Open-Source ML Models and Datasets for Software Engineering
por: González, Alexandra, et al.
Publicado: (2024)
por: González, Alexandra, et al.
Publicado: (2024)
Identifying architectural design decisions for achieving green ML serving
por: Durán, Francisco, et al.
Publicado: (2024)
por: Durán, Francisco, et al.
Publicado: (2024)
The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems
por: Omar, Rafiullah, et al.
Publicado: (2024)
por: Omar, Rafiullah, et al.
Publicado: (2024)
Insights into resource utilization of code small language models serving with runtime engines and execution providers
por: Durán, Francisco, et al.
Publicado: (2024)
por: Durán, Francisco, et al.
Publicado: (2024)
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
por: Weyssow, Martin, et al.
Publicado: (2023)
por: Weyssow, Martin, et al.
Publicado: (2023)
GAISSALabel: A tool for energy labeling of ML models
por: Duran, Pau, et al.
Publicado: (2024)
por: Duran, Pau, et al.
Publicado: (2024)
Bridging Expert Knowledge with Deep Learning Techniques for Just-In-Time Defect Prediction
por: Zhou, Xin, et al.
Publicado: (2024)
por: Zhou, Xin, et al.
Publicado: (2024)
How do Machine Learning Projects use Continuous Integration Practices? An Empirical Study on GitHub Actions
por: Bernardo, João Helis, et al.
Publicado: (2024)
por: Bernardo, João Helis, et al.
Publicado: (2024)
Aggregating empirical evidence from data strategy studies: a case on model quantization
por: del Rey, Santiago, et al.
Publicado: (2025)
por: del Rey, Santiago, et al.
Publicado: (2025)
Guiding the retraining of convolutional neural networks against adversarial inputs
por: López, Francisco Durán, et al.
Publicado: (2022)
por: López, Francisco Durán, et al.
Publicado: (2022)
Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review
por: Shivashankar, Karthik, et al.
Publicado: (2025)
por: Shivashankar, Karthik, et al.
Publicado: (2025)
Data Requirement Goal Modeling for Machine Learning Systems
por: Yamani, Asma, et al.
Publicado: (2025)
por: Yamani, Asma, et al.
Publicado: (2025)
Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning
por: Liang, Penghao, et al.
Publicado: (2024)
por: Liang, Penghao, et al.
Publicado: (2024)
The State of Documentation Practices of Third-party Machine Learning Models and Datasets
por: Oreamuno, Ernesto Lang, et al.
Publicado: (2023)
por: Oreamuno, Ernesto Lang, et al.
Publicado: (2023)
Bug Severity Prediction in Software Projects Using Supervised Machine Learning Models
por: Nice, Nafisha Tamanna
Publicado: (2026)
por: Nice, Nafisha Tamanna
Publicado: (2026)
Using Small Language Models to Reverse-Engineer Machine Learning Pipelines Structures
por: Lacroix, Nicolas, et al.
Publicado: (2026)
por: Lacroix, Nicolas, et al.
Publicado: (2026)
Geospatial Machine Learning Libraries
por: Stewart, Adam J., et al.
Publicado: (2025)
por: Stewart, Adam J., et al.
Publicado: (2025)
Data Virtualization for Machine Learning
por: Khan, Saiful, et al.
Publicado: (2025)
por: Khan, Saiful, et al.
Publicado: (2025)
A Framework for Using LLMs for Repository Mining Studies in Empirical Software Engineering
por: de Martino, Vincenzo, et al.
Publicado: (2024)
por: de Martino, Vincenzo, et al.
Publicado: (2024)
A Methodological Framework for LLM-Based Mining of Software Repositories
por: De Martino, Vincenzo, et al.
Publicado: (2025)
por: De Martino, Vincenzo, et al.
Publicado: (2025)
Machine Learning Systems are Bloated and Vulnerable
por: Zhang, Huaifeng, et al.
Publicado: (2022)
por: Zhang, Huaifeng, et al.
Publicado: (2022)
Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education
por: Mojica-Hanke, Anamaria, et al.
Publicado: (2024)
por: Mojica-Hanke, Anamaria, et al.
Publicado: (2024)
Automated Trustworthiness Testing for Machine Learning Classifiers
por: Cho, Steven, et al.
Publicado: (2024)
por: Cho, Steven, et al.
Publicado: (2024)
Machine Learning Operations: A Mapping Study
por: Chakraborty, Abhijit, et al.
Publicado: (2024)
por: Chakraborty, Abhijit, et al.
Publicado: (2024)
Optimising for Energy Efficiency and Performance in Machine Learning
por: Ferreira, Emile Dos Santos, et al.
Publicado: (2026)
por: Ferreira, Emile Dos Santos, et al.
Publicado: (2026)
Analysing Python Machine Learning Notebooks with Moose
por: Mignard, Marius, et al.
Publicado: (2025)
por: Mignard, Marius, et al.
Publicado: (2025)
An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Face
por: Stalnaker, Trevor, et al.
Publicado: (2025)
por: Stalnaker, Trevor, et al.
Publicado: (2025)
JetTrain: IDE-Native Machine Learning Experiments
por: Trofimov, Artem, et al.
Publicado: (2024)
por: Trofimov, Artem, et al.
Publicado: (2024)
Understanding Practitioners Perspectives on Monitoring Machine Learning Systems
por: Naveed, Hira, et al.
Publicado: (2025)
por: Naveed, Hira, et al.
Publicado: (2025)
Applications and Challenges of Fairness APIs in Machine Learning Software
por: Das, Ajoy, et al.
Publicado: (2025)
por: Das, Ajoy, et al.
Publicado: (2025)
Continuous Management of Machine Learning-Based Application Behavior
por: Anisetti, Marco, et al.
Publicado: (2023)
por: Anisetti, Marco, et al.
Publicado: (2023)
Automated Modernization of Machine Learning Engineering Notebooks for Reproducibility
por: Jin, Bihui, et al.
Publicado: (2026)
por: Jin, Bihui, et al.
Publicado: (2026)
Verifying Machine Learning Interpretability Requirements through Provenance
por: Vonderhaar, Lynn, et al.
Publicado: (2026)
por: Vonderhaar, Lynn, et al.
Publicado: (2026)
Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns
por: Chen, Keyu, et al.
Publicado: (2024)
por: Chen, Keyu, et al.
Publicado: (2024)
A Catalog of Fairness-Aware Practices in Machine Learning Engineering
por: Voria, Gianmario, et al.
Publicado: (2024)
por: Voria, Gianmario, et al.
Publicado: (2024)
Monitoring Machine Learning Systems: A Multivocal Literature Review
por: Naveed, Hira, et al.
Publicado: (2025)
por: Naveed, Hira, et al.
Publicado: (2025)
Ejemplares similares
-
Lessons Learned from Mining the Hugging Face Repository
por: Castaño, Joel, et al.
Publicado: (2024) -
Impact of ML Optimization Tactics on Greener Pre-Trained ML Models
por: Álvarez, Alexandra González, et al.
Publicado: (2024) -
Cataloguing Hugging Face Models to Software Engineering Activities: Automation and Findings
por: González, Alexandra, et al.
Publicado: (2025) -
Analyzing the Evolution and Maintenance of ML Models on Hugging Face
por: Castaño, Joel, et al.
Publicado: (2023) -
Towards a Classification of Open-Source ML Models and Datasets for Software Engineering
por: González, Alexandra, et al.
Publicado: (2024)