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Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.
A recursive neural network is a kind of deep neural network created by applying the same set of weights recursively over a structured input, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order.
A RNN (often a LSTM) where a series is decomposed into a number of scales where every scale informs the primary length between two consecutive points. A first order scale consists of a normal RNN, a second order consists of all points separated by two indices and so on. The Nth order RNN connects the first and last node.
In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...
The main difference between classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the Markov decision process, and they target large MDPs where exact methods become infeasible.
The atmosphere between the two men is worsening by the day as the inauguration nears. Biden keeps landing jabs seemingly calculated to get under Trump’s skin. And the president-elect has been ...
RNNs were rarely used in practice before the introduction of the ESN, because of the complexity involved in adjusting their connections (e.g., lack of autodifferentiation, susceptibility to vanishing/exploding gradients, etc.). RNN training algorithms were slow and often vulnerable to issues, such as branching errors. [16] Convergence could ...
One origin of RNN was statistical mechanics. In 1972, Shun'ichi Amari proposed to modify the weights of an Ising model by Hebbian learning rule as a model of associative memory, adding in the component of learning. [61] This was popularized as the Hopfield network by John Hopfield(1982). [62] Another origin of RNN was neuroscience.