It is known that rectified linear deep neural networks. (RL-DNNs) can consistently outperform the conventional pre- trained sigmoid DNNs even with a random ...

It is known that rectified linear deep neural networks. (RL-DNNs) can consistently outperform the conventional pre- trained sigmoid DNNs even with a random ...

1. Introduction. Recently, neural networks have revived as a popular model in · 2. Preliminaries: RL-DNNs. The structure of DNNs is a conventional multi-layer ...

Rectified Linear Neural Networks with Tied-Scalar Regularization for LVCSR ... Improving deep neural networks for LVCSR using rectified linear units and dropout.

parameterizing neural networks is thus an important problem in deep learning. ... Rectified linear neural networks with tied-scalar regularization for lvcsr.

Apr 27, 2023 · learning large-scale neural networks (NN). After introducing ... Rectified linear neural networks with tied-scalar regularization for LVCSR.

Yet a large mini-batch SGD method with tied scalar reg- ularization is proposed in [33] to train DNNs with rectified linear units (ReLUs) and achieves promising ...

Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks ... Rectified linear neural networks with tied-scalar regularization for LVCSR.

Very deep convolutional neural networks for LVCSR · Mengxiao Bi, Y. Qian, Kai ... Rectified linear neural networks with tied-scalar regularization for LVCSR ...