Understanding Knowledge Distillation in Neural Sequence Generation
1:18:05
Microsoft
Understanding Knowledge Distillation in Neural Sequence Generation
Sequence-level knowledge distillation (KD) — learning a student model with targets decoded from a pre-trained teacher model — has been widely used in sequence generation applications (e.g. model compression, non-autoregressive translation (NAT), low-resource translation, etc). However, the underlying reasons behind this success have, as of ...
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201 reactions · 41 shares | Microsoft and CMU researchers have begun to unravel three mysteries in deep learning related to ensemble, knowledge distillation, and self-distillation. Discover how their work has led to the first theoretical proof with empirical evidence for ensemble in deep learning: https://aka.ms/AAavuq3 | Microsoft Research | Facebook
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201 reactions · 41 shares | Microsoft and CMU researchers have begun to unravel three mysteries in deep learning related to ensemble, knowledge distillation, and self-distillation. Discover how their work has led to the first theoretical proof with empirical evidence for ensemble in deep learning: https://aka.ms/AAavuq3 | Microsoft Research | Facebook
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