Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

This paper presents a survey of methods for pruning deep neural networks. It begins by
categorising over 150 studies based on the underlying approach used and then focuses on …

Modern speech enhancement algorithms achieve remarkable noise suppression by means
of large recurrent neural networks (RNNs). However, large RNNs limit practical deployment …

Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to
freshwater environments. In order to develop an intelligent early warning system for HABs …

Multi-label document classification (MLDC) problems can be challenging, especially for long
documents with a large label set and a long-tail distribution over labels. In this paper, we …

The standard normalization method for neural network (NN) models used in Natural
Language Processing (NLP) is layer normalization (LN). This is different than batch …

The stock market is a platform that allows individuals and organizations to buy stocks of
publicly listed companies. It is imperative for investors and traders to utilize the platform to …

Low-precision deep neural network (DNN) training has gained tremendous attention as
reducing precision is one of the most effective knobs for boosting DNNs' training time/energy …

This paper proposes an audio tampering detection method based on the ENF phase and BI-
LSTM network from the perspective of temporal feature representation learning. First, the …

Hearing problems are commonly diagnosed with the use of tonal audiometry, which
measures a patient's hearing threshold in both air and bone conduction at various …