AutoAblation: Automated Parallel Ablation Studies for Deep Learning
| Autor: | Jim Dowling, Vladimir Vlassov, Sina Sheikholeslami, Amir H. Payberah, Moritz Meister, Tianze Wang |
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| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science medicine.medical_treatment Deep learning 020206 networking & telecommunications 02 engineering and technology Ablation Machine learning computer.software_genre Execution time Regularization (mathematics) 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering medicine Artificial intelligence business computer |
| Zdroj: | EuroMLSys@EuroSys |
| DOI: | 10.1145/3437984.3458834 |
| Popis: | Ablation studies provide insights into the relative contribution of different architectural and regularization components to machine learning models' performance. In this paper, we introduce AutoAblation, a new framework for the design and parallel execution of ablation experiments. AutoAblation provides a declarative approach to defining ablation experiments on model architectures and training datasets, and enables the parallel execution of ablation trials. This reduces the execution time and allows more comprehensive experiments by exploiting larger amounts of computational resources. We show that AutoAblation can provide near-linear scalability by performing an ablation study on the modules of the Inception-v3 network trained on the TenGeoPSAR dataset. |
| Databáze: | OpenAIRE |
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