Sentence examples for vanishing problem from inspiring English sources

The model structure has the following characteristics: first, the gradient vanishing problem is solved by using a rectified linear unit (ReLU) nonlinearity activation function with non-saturating characteristics and a learning speed that is faster than the activation function of the existing saturating nonlinearity characteristic.

We remark that when b = − 1, c = 0 and third variable of the function F in (1) vanishes, problem (1) reduces to a three-point fractional integral boundary value problem (see [17] with F = f a given continuous function).

This lack of progress using deep networks can be mainly attributed to vanishing gradient problem or exploding descent problem.

Because of the vanishing gradient problem, RNNs look back just a few steps.

Thankfully, there are a variety of methods that can help us address the vanishing gradient problem.

We also use ReLUs in all the convolutional layers of BN1 and BN2 to avoid the vanishing gradient problem.

Deep Learning methods can overcome vanishing gradient problem so they can train with dozens of layers of non-linear hierarchical features.

The training of the two networks fails on both classification tasks, due to the vanishing gradient problem.

Generally, it is difficult to learn a large number of parameters in a deep architecture which has multiple hidden layers due to the vanishing gradient problem.

In particular, stronger learning models [10, 11] as well as effective techniques for suppressing overfitting [12] and avoiding the vanishing gradient problem [13] have significantly improved the performance of CNNs.

The vanishing gradients problem inherent in Simple Recurrent Networks (SRN) trained with back-propagation, has led to a significant shift towards the use of Long Short-Term Memory (LSTM) and Echo State Networks (ESN), which overcome this problem through either second order error-carousel schemes or different learning algorithms, respectively.