منابع مشابه
BackPropagation Through Time
This report provides detailed description and necessary derivations for the BackPropagation Through Time (BPTT) algorithm. BPTT is often used to learn recurrent neural networks (RNN). Contrary to feed-forward neural networks, the RNN is characterized by the ability of encoding longer past information, thus very suitable for sequential models. The BPTT extends the ordinary BP algorithm to suit t...
متن کاملUnbiasing Truncated Backpropagation through Time
Truncated Backpropagation Through Time (truncated BPTT, Jaeger (2005)) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of Backpropagation Through Time (BPTT Werbos (1990)) while relieving the need for a complete backtrack through the whole data sequence at every step. However, truncation favors short-term dependencies: the grad...
متن کاملMemory-Efficient Backpropagation Through Time
We propose a novel approach to reduce memory consumption of the backpropagation through time (BPTT) algorithm when training recurrent neural networks (RNNs). Our approach uses dynamic programming to balance a trade-off between caching of intermediate results and recomputation. The algorithm is capable of tightly fitting within almost any user-set memory budget while finding an optimal execution...
متن کاملUnbiasing Truncated Backpropagation Through Time
Truncated Backpropagation Through Time (truncated BPTT, [Jae05]) is a widespread method for learning recurrent computational graphs. Truncated BPTT keeps the computational benefits of Backpropagation Through Time (BPTT [Wer90]) while relieving the need for a complete backtrack through the whole data sequence at every step. However, truncation favors short-term dependencies: the gradient estimat...
متن کاملRelating Real-Time Backpropagation and Backpropagation-Through-Time: An Application of Flow Graph Interreciprocity
We show that signal ow graph theory provides a simple way to relate two popular algorithms used for adapting dynamic neural networks, real-time backpropagation and backpropagation-through-time. Starting with the ow graph for real-time backpropagation, we use a simple transposition to produce a second graph. The new graph is shown to be interreciprocal with the original and to correspond to the ...
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ژورنال
عنوان ژورنال: Current Opinion in Neurobiology
سال: 2019
ISSN: 0959-4388
DOI: 10.1016/j.conb.2019.01.011