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Hiroshi Abe

2 accepted papers

2020

Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network

ICLR 2020spotlight

One of the biggest issues in deep learning theory is the generalization ability of networks with huge model size. The classical learning theory suggests that overparameterized models cause overfitting. However, practically used large deep models avoid overfitting, which is not well explained by the…

Cited by 47SourceScholar
2020

Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error

IJCAI 2020poster

Compression techniques for deep neural network models are becoming very important for the efficient execution of high-performance deep learning systems on edge-computing devices. The concept of model compression is also important for analyzing the generalization error of deep learning, known as th…

Cited by 0SourcePDFScholar