ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
Neural networks are powerful tools for sequence modeling, and Recurrent Neural Networks (RNNs) were once the go-to solution for learning from time-dependent data. But as researchers and engineers ...
The aims of this study are to predict the stock price trend in the stock market in an emerging economy. Using the Long Short Term Memory (LSTM) algorithm, and the corresponding technical analysis ...
Abstract: In this letter, we propose a bio-inspired derivative-free optimization algorithm capable of minimizing objective functions with vanishing or exploding gradients. The proposed method searches ...
This paper presents a sound source localization (SSL) model based on residual network and channel attention mechanism. The method takes the combination of log-Mel spectrogram and generalized ...
When diving into the theory behind Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, two main questions arise: 1. Why do RNNs suffer from vanishing and exploding gradients?
Abstract: Deep neural networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a ...
How do humans and animals perform trial-and-error learning when the space of possibilities is infinite? In a previous study, we used an interval timing production ...
Machine learning is on track to consume all the energy being supplied, a model that is costly, inefficient, and unsustainable. To a large extent, this is because the field is new, exciting, and ...
The human brain can be considered as a complex dynamic and recurrent neural network. There are several models for neural networks of the human brain, that cover sensory to cortical information ...
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