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1,359 claims from 845 papers are on the record. 46 have been checked so far; the other 1,313 have no check with a result yet.

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Status: Unchecked ×Keyword: knowledge distillation ×Clear all

3 claims from 3 papers

1. Computer Science › Advanced Neural Network Applications

   Distilling the Knowledge in a Neural Network

   Hinton, Vinyals and Jeff · arXiv (Cornell University) · 2015

   The paper develops a way to compress an ensemble of neural networks into one model, and proposes an ensemble that adds specialist models to full models.

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   1. UncheckedThe paper says its specialist models, unlike a mixture of experts, can be trained rapidly and in parallel.“Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.”

2. Computer Science › Topic Modeling

   Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

   Hsieh, Li, Yeh et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes.”

3. Computer Science › Advanced Neural Network Applications

   ResMLP: Feedforward networks for image classification with data-efficient training

   Touvron, Bojanowski, Mathilde et al. · arXiv (Cornell University) · 2021

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   1. Unchecked“When trained with a modern training strategy using heavy data-augmentation and optionally distillation, it attains surprisingly good accuracy/complexity trade-offs on ImageNet.”