Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Tietämyksen tislaus× | Asiantuntijoiden sekoitus× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2015 | 2017 |
| Kehittäjä≠ | Hinton, G., Vinyals, O. & Dean, J. | Shazeer, N. et al. |
| Tyyppi≠ | Neural network compression (teacher–student) | Sparse neural network architecture (conditional computation) |
| Alkuperäislähde≠ | Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop. link ↗ | Shazeer, N. et al. (2017). Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. ICLR. arXiv:1701.06538 link ↗ |
| Rinnakkaisnimet≠ | Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation | Uzman Karışımı (Mixture of Experts — MoE), uzman karışımı, MoE, sparse mixture of experts |
| Liittyvät≠ | 5 | 3 |
| Tiivistelmä≠ | Knowledge Distillation is a model-compression technique, introduced by Geoffrey Hinton and colleagues in 2015, that trains a small student model using the soft-label outputs of a large teacher model. Distilled models such as DistilBERT and TinyBERT reach roughly 97% of the larger model's performance while running far faster. | 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. |
| ScholarGateAineisto ↗ |
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