Copertina

Machine Learning Robustness: A Primer

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Braiek, Houssem Ben, Khomh, Foutse
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence Software Engineering
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems
di: Abbassi, Altaf Allah, et al.
Pubblicazione: (2024)

What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach
di: Wu, Xingfang, et al.
Pubblicazione: (2024)

Prism: Dynamic and Flexible Benchmarking of LLMs Code Generation with Monte Carlo Tree Search
di: Majdinasab, Vahid, et al.
Pubblicazione: (2025)

Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent
di: Shah, Mehil B, et al.
Pubblicazione: (2025)

Dynasto: Validity-Aware Dynamic-Static Parameter Optimization for Autonomous Driving Testing
di: Humeniuk, Dmytro, et al.
Pubblicazione: (2026)

Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
di: Bouchoucha, Rached, et al.
Pubblicazione: (2024)

DeepCodeProbe: Towards Understanding What Models Trained on Code Learn
di: Majdinasab, Vahid, et al.
Pubblicazione: (2024)

GIST: Generated Inputs Sets Transferability in Deep Learning
di: Tambon, Florian, et al.
Pubblicazione: (2023)

RefAgent: A Multi-agent LLM-based Framework for Automatic Software Refactoring
di: Oueslati, Khouloud, et al.
Pubblicazione: (2025)

Fault Localization in Deep Learning-based Software: A System-level Approach
di: Morovati, Mohammad Mehdi, et al.
Pubblicazione: (2024)

SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs
di: Aghili, Roozbeh, et al.
Pubblicazione: (2025)

PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4
di: Abukhalaf, Seif, et al.
Pubblicazione: (2024)

Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code
di: Majdinasab, Vahid, et al.
Pubblicazione: (2024)

On the Effectiveness of Log Representation for Log-based Anomaly Detection
di: Wu, Xingfang, et al.
Pubblicazione: (2023)

LLMs and Stack Overflow Discussions: Reliability, Impact, and Challenges
di: Da Silva, Leuson, et al.
Pubblicazione: (2024)

Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study
di: Shah, Mehil B., et al.
Pubblicazione: (2024)

Mining Action Rules for Defect Reduction Planning
di: Oueslati, Khouloud, et al.
Pubblicazione: (2024)

An Efficient Model Maintenance Approach for MLOps
di: Majidi, Forough, et al.
Pubblicazione: (2024)

A Survey of Bugs in AI-Generated Code
di: Gao, Ruofan, et al.
Pubblicazione: (2025)

FairFLRep: Fairness aware fault localization and repair of Deep Neural Networks
di: Openja, Moses, et al.
Pubblicazione: (2025)

Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs
di: Ngassom, Sylvain Kouemo, et al.
Pubblicazione: (2024)

TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models
di: Tambon, Florian, et al.
Pubblicazione: (2024)

ReCatcher: Towards LLMs Regression Testing for Code Generation
di: Abbassi, Altaf Allah, et al.
Pubblicazione: (2025)

Quality Issues in Machine Learning Software Systems
di: Côté, Pierre-Olivier, et al.
Pubblicazione: (2023)

Refactoring with LLMs: Bridging Human Expertise and Machine Understanding
di: Piao, Yonnel Chen Kuang, et al.
Pubblicazione: (2025)

Tracing Stereotypes in Pre-trained Transformers: From Biased Neurons to Fairer Models
di: Voria, Gianmario, et al.
Pubblicazione: (2026)

Machine Learning with Requirements: a Manifesto
di: Giunchiglia, Eleonora, et al.
Pubblicazione: (2023)

Machine Learning Systems: A Survey from a Data-Oriented Perspective
di: Cabrera, Christian, et al.
Pubblicazione: (2023)

ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
di: Abdelkader, Hala, et al.
Pubblicazione: (2024)

Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning
di: de Conto, Eduardo, et al.
Pubblicazione: (2024)

Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring
di: Moin, Armin, et al.
Pubblicazione: (2022)

Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends
di: Taraghi, Mina, et al.
Pubblicazione: (2024)

The Impact of Software Testing with Quantum Optimization Meets Machine Learning
di: Bandarupalli, Gopichand
Pubblicazione: (2025)

asanAI: In-Browser, No-Code, Offline-First Machine Learning Toolkit
di: Koch, Norman, et al.
Pubblicazione: (2025)

Model Lake: a New Alternative for Machine Learning Models Management and Governance
di: Garouani, Moncef, et al.
Pubblicazione: (2025)

Sustainable Machine Learning Retraining: Optimizing Energy Efficiency Without Compromising Accuracy
di: Poenaru-Olaru, Lorena, et al.
Pubblicazione: (2025)

Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence
di: Khokhar, Pir Bakhsh, et al.
Pubblicazione: (2025)

Bugs in Large Language Models Generated Code: An Empirical Study
di: Tambon, Florian, et al.
Pubblicazione: (2024)

Automated File-Level Logging Generation for Machine Learning Applications using LLMs: A Case Study using GPT-4o Mini
di: Rodriguez, Mayra Sofia Ruiz, et al.
Pubblicazione: (2025)

Causal Fuzzing for Verifying Machine Unlearning
di: Mazhar, Anna, et al.
Pubblicazione: (2025)