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Found 3 papers, 1 papers with code
A Selective Survey on Versatile Knowledge Distillation Paradigm for Neural Network Models
This paper aims to provide a selective survey about knowledge distillation(KD) framework for researchers and practitioners to take advantage of it for developing new optimized models in the deep neural network field.
Weight Equalizing Shift Scaler-Coupled Post-training Quantization
As a result, our proposed method achieved a top-1 accuracy of 69. 78% ~ 70. 96% in MobileNets and showed robust performance in varying network models and tasks, which is competitive to channel-wise quantization results.
Advancing GraphSAGE with A Data-Driven Node Sampling
As an efficient and scalable graph neural network, GraphSAGE has enabled an inductive capability for inferring unseen nodes or graphs by aggregating subsampled local neighborhoods and by learning in a mini-batch gradient descent fashion.
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