In recent years, denoising methods based on deep learning have achieved unparalleled
performance at the cost of large computational complexity. In this work, we propose an …

The fast wavelet transform is an essential workhorse in signal processing. Wavelets are
local in the spatial-or temporal-and the frequency-domain. This property enables frequency …

Abstract Convolutional Neural Networks (CNNs) are known for requiring extensive
computational resources, and quantization is among the best and most common methods for …

Existing wavelet pooling methods discard the high-frequency sub-bands, which can improve
the noise-robustness of convolutional neural networks (CNNs) but lose the essential …

Machine learning algorithms now make it possible for computers to solve problems, which
were thought to be impossible to automize. Neural Speech processing, convolutional neural …

In recent years, deep learning models have achieved remarkable successes in various
applications, such as pattern recognition, computer vision, and signal processing. However …

Regularization techniques help prevent overfitting and therefore improve the ability of
convolutional neural networks (CNNs) to generalize. One reason for overfitting is the …

We introduce canonical weight normalization for convolutional neural networks. Inspired by
the canonical tensor decomposition, we express the weight tensors in so-called canonical …

Transformer and its variants are fundamental neural architectures in deep learning. Recent
works show that learning attention in the Fourier space can improve the long sequence …

This paper proposes a new method of dimensionality reduction when performing Text
Classification, by applying the discrete wavelet transform to the document-term frequencies …