2 papers
cs.LG2019
Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
Taiji Suzuki, Hiroshi Abe, Tomoaki Nishimura
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 mode…
stat.ML2018
Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error
Taiji Suzuki, Hiroshi Abe, Tomoya Murata +6
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. T…