Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Utaftaji wa Bayesian× | Utafutaji wa Usanifu wa Neural× | |
|---|---|---|
| Nyanja≠ | Uboreshaji | Ujifunzaji wa Kina |
| Familia≠ | Process / pipeline | Machine learning |
| Mwaka wa asili≠ | 1975 (foundational); 2012 (ML standard) | 2017 |
| Mwanzilishi≠ | Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012) | Zoph, B. & Le, Q.V. |
| Aina≠ | Sequential model-based black-box optimization | Automated architecture optimization (deep learning) |
| Chanzo asilia≠ | Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25. link ↗ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ |
| Majina mbadala | Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search |
| Zinazohusiana≠ | 2 | 5 |
| Muhtasari≠ | Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones. | Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All. |
| ScholarGateSeti ya data ↗ |
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