Such a problem is referred to as the exploding gradient problem. RNNs in spite of being powerful are tough to train due to the exploding and vanishing gradient problem that does not allow the solution to converge [81, 82] and were limited to machine learning applications. Keeping this in ...
Improves the AI's ability to evaluate board positions and choose optimal actions. Residual Neural Networks: Utilizes residual connections to allow deeper networks without the vanishing gradient problem. Enables the AI to learn more complex patterns and strategies. Domain Randomization and Data Augmentation...
In this way, phase-field simulations can be redefined as multivariable time-series problem99,113,114. In order to overcome the shortcoming of vanishing or exploding gradients that often exist when using RNN, LSTM is proposed as a subclass of RNN, which has better accuracy and long-term ...
The problem also limits RNNs' memory capacity because error signals may not be able to back-propagated far enough. There have been two lines of researches to address this problem. One is to design learning algorithms that can avoid gradient exploding, e.g., using gradient clipping [14], ...
However, the memory capacity of simple RNNs is limited because of the gradient vanishing and exploding problem. We propose to use an external memory to improve memorization capability of RNNs. We conducted experiments on the ATIS dataset, and observed that the proposed model was able to achieve...
Problem Definition. Table 3 presents the primary notations used in our study, followed by the essential definitions of factors for predicting the next actions of a given terrorist group and introducing the details of the base model and our framework. Table 3. Primary notations used in the study...
Vanishing Gradients occurs when the values of a gradient are too small and the model stops learning or takes way too long because of that. Exploding Gradients Exploding Gradients occurs when the algorithm assigns a stupidly high importance to the weights, without much reason. ...
The successful discovery and isolation of graphene in 2004, and the subsequent synthesis of layered semiconductors and heterostructures beyond graphene have led to the exploding field of two-dimensional (2D) materials that explore their growth, new atomi
Although LSTMs improved upon RNNs, the problem of modeling very long sequences remained. Further, LSTMs just like vanilla RNNs, process words sequentially i.e. one after the other. Thus, inference on long sequences is slow. Neural Self Attention Mechanism ...
In terms of architectural choices, skip connections [2] are utilized to mitigate the vanishing gradient problem during training. While skip connections help in preserving gradients across layers, we considered other methods like batch normalization and residual scaling. However, after evaluating these tec...