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The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time. Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at mitigating the vanishing gradient problem [2] commonly encountered by traditional RNNs.
The standard method for training RNN by gradient descent is the "backpropagation through time" (BPTT) algorithm, which is a special case of the general algorithm of backpropagation. A more computationally expensive online variant is called "Real-Time Recurrent Learning" or RTRL, [ 78 ] [ 79 ] which is an instance of automatic differentiation in ...
CTC scores can then be used with the back-propagation algorithm to update the neural network weights. Alternative approaches to a CTC-fitted neural network include a hidden Markov model (HMM). In 2009, a Connectionist Temporal Classification (CTC)-trained LSTM network was the first RNN to win pattern recognition contests when it won several ...
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. [1] The GRU is like a long short-term memory (LSTM) with a gating mechanism to input or forget certain features, [2] but lacks a context vector or output gate, resulting in fewer parameters than LSTM. [3]
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Some of President-elect Donald Trump’s most vulnerable Cabinet picks are racing to smooth out or overwrite past statements before contentious Senate confirmation fights.
United States stock markets will be closing early on Friday, Nov. 29 after being closed altogether on Thursday, Nov. 28, in observance of the Thanksgiving holiday.. The Nasdaq and New York Stock ...
He and Schmidhuber later designed the LSTM architecture to solve this problem, [4] [21] which has a "cell state" that can function as a generalized residual connection. The highway network (2015) [22] [23] applied the idea of an LSTM unfolded in time to feedforward neural networks, resulting in the highway network. ResNet is equivalent to an ...