Learning Sparse Neural Networks via Sensitivity-Driven Regularization

NeurIPS 2018 Enzo TartaglioneSkjalg LepsøyAttilio FiandrottiGianluca Francini

The ever-increasing number of parameters in deep neural networks poses challenges for memory-limited applications. Regularize-and-prune methods aim at meeting these challenges by sparsifying the network weights... (read more)

PDF Abstract NeurIPS 2018 PDF NeurIPS 2018 Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet