As shown in Figure 1, quantizing weights into ternary values {-1, 0, 1} can only save 1.4

Finally, we validated our method using a previously published dataset.

In this work, we propose Covariance-Preserving Adversarial Augmentation Networks to overcome existing limits of low-shot learning. Specifically, a novel Generative Adversarial Network is designed to model the latent distribution of each novel class given its related base counterparts.

WN-DNN architecture influences their generalization properties.

Neural Information Processing Systems http://nips.cc/

We propose and study the problem of distribution-preserving lossy compression.

Multi-Task Learning (MTL) is appealing for deep learning regularization.

Although dropout is widely used as a regularization technique for fully connected layers, it is often less effective for convolutional layers.

In this work, we address the problem of modifying textual attributes of sentences. Given an input sentence and a set of attribute labels, we attempt to generate sentences that are compatible with the conditioning information.

Recently, several researchers have started to view VDNNs from a dynamical systems perspective.