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.

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 ...

Nationals MP Keith Pitt is urging Australians to look into the dangers of E-bikes. NSW Fire Service advice says lithium-ion batteries are now the greatest rising risk of fires in the state. “They give ...

Researchers from MIT and Facebook AI have developed projUNN, an effective method for training deep networks using unitary matrices. The projUNN method includes two variants, projUNN-D and projUNN-T, ...

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 ...

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 ...

Your browser does not support the audio element. Hello Stardust! Today we’ll see mathematical reason behind exploding and vanishing gradient problem but first let ...