%0 Journal Article %1 Gordon2017 %A Gordon, Ariel %A Eban, Elad %A Nachum, Ofir %A Chen, Bo %A Wu, Hao %A Yang, Tien-Ju %A Choi, Edward %D 2017 %K cs.LG, stat.ML %T MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks %X We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our method is scalable to large networks, adaptable to specific resource constraints (e.g. the number of floating-point operations per inference), and capable of increasing the network's performance. When applied to standard network architectures on a wide variety of datasets, our approach discovers novel structures in each domain, obtaining higher performance while respecting the resource constraint.

@article{Gordon2017, abstract = {We present MorphNet, an approach to automate the design of neural network structures. MorphNet iteratively shrinks and expands a network, shrinking via a resource-weighted sparsifying regularizer on activations and expanding via a uniform multiplicative factor on all layers. In contrast to previous approaches, our method is scalable to large networks, adaptable to specific resource constraints (e.g. the number of floating-point operations per inference), and capable of increasing the network's performance. When applied to standard network architectures on a wide variety of datasets, our approach discovers novel structures in each domain, obtaining higher performance while respecting the resource constraint.}, added-at = {2020-10-15T14:36:56.000+0200}, author = {Gordon, Ariel and Eban, Elad and Nachum, Ofir and Chen, Bo and Wu, Hao and Yang, Tien-Ju and Choi, Edward}, biburl = {https://www.bibsonomy.org/bibtex/2cf787220cc3dcc6c7a9171fbfb8b0406/annakrause}, eprint = {1711.06798v3}, eprintclass = {cs.LG}, eprinttype = {arXiv}, file = {:http\://arxiv.org/pdf/1711.06798v3:PDF}, interhash = {84e1eacfc0d7669d790049ec3ddc94e6}, intrahash = {cf787220cc3dcc6c7a9171fbfb8b0406}, keywords = {cs.LG, stat.ML}, timestamp = {2020-10-15T14:44:42.000+0200}, title = {MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks}, year = 2017 }