تقرير
Ranking architectures using meta-learning
| العنوان: | Ranking architectures using meta-learning |
|---|---|
| المؤلفون: | Dubatovka, Alina, Kokiopoulou, Efi, Sbaiz, Luciano, Gesmundo, Andrea, Bartok, Gabor, Berent, Jesse |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Neural architecture search has recently attracted lots of research efforts as it promises to automate the manual design of neural networks. However, it requires a large amount of computing resources and in order to alleviate this, a performance prediction network has been recently proposed that enables efficient architecture search by forecasting the performance of candidate architectures, instead of relying on actual model training. The performance predictor is task-aware taking as input not only the candidate architecture but also task meta-features and it has been designed to collectively learn from several tasks. In this work, we introduce a pairwise ranking loss for training a network able to rank candidate architectures for a new unseen task conditioning on its task meta-features. We present experimental results, showing that the ranking network is more effective in architecture search than the previously proposed performance predictor. Comment: NeurIPS 2019 Meta-Learning workshop |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1911.11481 |
| رقم الأكسشن: | edsarx.1911.11481 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |