To this end, a new form of ℓ 0 -norm-based regularization is firstly developed, which is capable of inducing strong sparseness in the network during training.

To this end, a new form of ℓ0-norm-based regularization is firstly developed, which is capable of inducing strong sparseness in the network during training.

To this end, a new form of ℓ 0 -norm-based regularization is firstly developed, which is capable of inducing strong sparseness in the network during training.

In this context, network compression techniques have been gaining interest due to their ability for reducing deployment costs while keeping inference accuracy ...

The present paper is dedicated to the development of a novel compression scheme for neural networks. To this end, a new form of ℓ 0-norm-based regularization is ...

On the Compression of Neural Networks Using l0-Norm Regularization and Weight Pruning · F. Oliveira, E. Batista, R. Seara · Published in Neural Networks 10 ...

In this paper, we briefly summarize the existing advanced techniques that are useful in model compression at first. After that, we give a detailed description ...

This paper considers the batch gradient method with the smoothing ℓ0 regularization (BGSL0) for training and pruning feedforward neural networks. We show why ...

The present paper is dedicated to the development of a novel compression scheme for neural networks. To this end, a new form of ℓ0-norm-based regularization is ...

The present paper is dedicated to the development of a novel compression scheme for neural networks. To this end, a new $\ell_0$-norm-based regularization ...