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The standard LSTM architecture was introduced in 2000 by Felix Gers, Schmidhuber, and Fred Cummins. [20] Today's "vanilla LSTM" using backpropagation through time was published with his student Alex Graves in 2005, [21] [22] and its connectionist temporal classification (CTC) training algorithm [23] in 2006. CTC was applied to end-to-end speech ...
(Rupesh Kumar Srivastava, Klaus Greff, and Schmidhuber, 2015) used LSTM principles [67] to create the Highway network, a feedforward neural network with hundreds of layers, much deeper than previous networks. [69] [70] [71] Concurrently, the ResNet architecture was developed. It is equivalent to an open-gated or gateless highway network. [72]
That LSTM was not yet the modern architecture, which required a "forget gate", introduced in 1999, [48] which became the standard RNN architecture. Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1995 and set accuracy records in multiple applications domains. [46] [49] It became the default choice for RNN ...
Hochreiter developed the long short-term memory (LSTM) neural network architecture in his diploma thesis in 1991 leading to the main publication in 1997. [3] [4] LSTM overcomes the problem of numerical instability in training recurrent neural networks (RNNs) that prevents them from learning from long sequences (vanishing or exploding gradient).
LSTM: Sepp Hochreiter and Jürgen Schmidhuber invent long short-term memory (LSTM) recurrent neural networks, [36] greatly improving the efficiency and practicality of recurrent neural networks. 1998: MNIST database
The GAN principle was originally published in 1991 by Jürgen Schmidhuber who called it "artificial curiosity": two neural networks contest with each other in the form of a zero-sum game, where one network's gain is the other network's loss. [95] [96] The first network is a generative model that models a probability distribution over output ...
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 ...
With Jürgen Schmidhuber and Fred Cummins, he introduced the forget gate to the long short-term memory recurrent neural network architecture. [2] [3] This modification of the original [4] architecture has been shown to be crucial to the success of the LSTM at such tasks as speech and handwriting recognition. [3]