Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks, such as Elman networks. The...

In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is...

feedforward multiplication remains the core, essential for backpropagation or backpropagation through time. Thus neural networks cannot contain feedback like...

each time-step of an input sequence processed by the network (the combination of unfolding and backpropagation is termed backpropagation through time). This...

descent is the "backpropagation through time" (BPTT) algorithm, which is a special case of the general algorithm of backpropagation. A more computationally...

an optimization algorithm like gradient descent combined with backpropagation through time to compute the gradients needed during the optimization process...

first described the process of training artificial neural networks through backpropagation of errors. He also was a pioneer of recurrent neural networks....

F ( x t ) + x t {\textstyle y_{t+1}=F(x_{t})+x_{t}} . During backpropagation through time, this becomes the residual formula y = F ( x ) + x {\textstyle...

Schmidhuber, and Fred Cummins. Today's "vanilla LSTM" using backpropagation through time was published with his student Alex Graves in 2005, and its connectionist...

PhD thesis, Harvard University, 1974 Werbos, P.J. (1990). "Backpropagation through time: what it does and how to do it". Proceedings of the IEEE. 78...

Neural backpropagation is the phenomenon in which, after the action potential of a neuron creates a voltage spike down the axon (normal propagation),...

The gradient is computed using backpropagation through structure (BPTS), a variant of backpropagation through time used for recurrent neural networks...

of RNNs a number of learning algorithms are available: backpropagation through time, real-time recurrent learning. Convergence is not guaranteed due to...

Backpropagation training algorithms fall into three categories: steepest descent (with variable learning rate and momentum, resilient backpropagation);...

using synthetic gradients performs considerably better than Backpropagation through time (BPTT). Robustness can be improved with use of layer normalization...

actual target values in a given dataset. Gradient-based methods such as backpropagation are usually used to estimate the parameters of the network. During...

functions are differentiable. The standard method is called "backpropagation through time" or BPTT, a generalization of back-propagation for feedforward...

O(|E|)} time. During the breadth-first search, the order in which vertices are visited is logged in a stack data structure. The backpropagation step then...

1986, David E. Rumelhart et al. popularised backpropagation but did not cite the original work. The time delay neural network (TDNN) was introduced in...

"AI winter". Later, advances in hardware and the development of the backpropagation algorithm, as well as recurrent neural networks and convolutional neural...

transformer. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by the regularization that...

1996 paper written by Christoph Goller and Andreas Küchler. backpropagation through time (BPTT) A gradient-based technique for training certain types...

{\displaystyle 1\times 9} . It was trained on ~800 samples for 20000--50000 backpropagation steps. Each steps was computed in a batch over the entire training...

A network is trained by modifying these weights through empirical risk minimization or backpropagation in order to fit some preexisting dataset. The term...

activation within the network, the scale of gradient signals during backpropagation, and the quality of the final model. Proper initialization is necessary...

competitive learning rather than the error-correction learning (e.g., backpropagation with gradient descent) used by other artificial neural networks. The...

(NCCL). It is mainly used for allreduce, especially of gradients during backpropagation. It is asynchronously run on the CPU to avoid blocking kernels on the...

that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). A model may also be augmented with "adapters" that consist of far...

rule alone Seppo Linnainmaa in 1970 is said to have developed the Backpropagation Algorithm but the origins of the algorithm go back to the 1960s with...

hand-designed. In 1989, Yann LeCun et al. at Bell Labs first applied the backpropagation algorithm to practical applications, and believed that the ability...