Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Căutarea Arhitecturilor Neuronale× | Amestec de Experți× | |
|---|---|---|
| Domeniu | Învățare profundă | Învățare profundă |
| Familie | Machine learning | Machine learning |
| Anul apariției | 2017 | 2017 |
| Autorul original≠ | Zoph, B. & Le, Q.V. | Shazeer, N. et al. |
| Tip≠ | Automated architecture optimization (deep learning) | Sparse neural network architecture (conditional computation) |
| Sursa seminală≠ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ | Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538 link ↗ |
| Denumiri alternative≠ | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search | Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts |
| Înrudite≠ | 5 | 3 |
| Rezumat≠ | 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. | Mixture of Experts (MoE) is a sparse neural-network architecture, introduced by Shazeer and colleagues in 2017 with the sparsely-gated MoE layer, in which only a subset of expert sub-networks is activated for each input. As seen in models such as Switch Transformer and Mixtral, it holds computation cost fixed even as the total parameter count grows. |
| ScholarGateSet de date ↗ |
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