New-found immune cells called ‘ruptoblasts’ explode when triggered, ejecting toxic chemicals capable of delivering death to surrounding cells in just minutes. The cells’ discoverers say that this ...

Abstract: Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point problem.

ABSTRACT: Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, ...

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 ...

Machine Learning Practical - Coursework 2 Report: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow ...

The exploding gradient problem is a significant challenge in neural networks, particularly affecting recurrent neural networks. This issue hampers the training process by causing the weights to grow ...

Neural networks must be initialized before one can start training them. As with any aspect of deep learning, however, there are many ways in which this can be done. Random initialization of the neural ...

RNNs are specifically designed to handle time-series and sequential data effectively. Training RNNs presents challenges, particularly in reliable information representation and generalisation with ...