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Jürgen Schmidhuber (born 17 January 1963) [1] is a German computer scientist noted for his work in the field of artificial intelligence, specifically artificial neural networks. He is a scientific director of the Dalle Molle Institute for Artificial Intelligence Research in Switzerland . [ 2 ]
2001: Gers and Schmidhuber trained LSTM to learn languages unlearnable by traditional models such as Hidden Markov Models. [21] [63] Hochreiter et al. used LSTM for meta-learning (i.e. learning a learning algorithm). [76] 2005: Daan Wierstra, Faustino Gomez, and Schmidhuber trained LSTM by neuroevolution without a teacher. [7]
Hochreiter introduced modern Hopfield networks with continuous states [18] and applied them to the task of immune repertoire classification. [19] Hochreiter worked with Jürgen Schmidhuber in the field of reinforcement learning on actor-critic systems that learn by "backpropagation through a model". [6] [20]
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 architecture. Around 2006, LSTM started to revolutionize speech recognition, outperforming traditional models in certain speech applications.
Long short-term memory (LSTM) networks were invented by Hochreiter and Schmidhuber in 1995 and set accuracy records in multiple applications domains. [35] [36] It became the default choice for RNN architecture. Bidirectional recurrent neural networks (BRNN) uses two RNN that processes the same input in opposite directions. [37]
Sepp Hochreiter and Jürgen Schmidhuber invent long short-term memory (LSTM) recurrent neural networks, [42] greatly improving the efficiency and practicality of recurrent neural networks. 1998: MNIST database
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Sepp Hochreiter discovered the vanishing gradient problem in 1991 [20] and argued that it explained why the then-prevalent forms of recurrent neural networks did not work for long sequences. He and Schmidhuber later designed the LSTM architecture to solve this problem, [ 4 ] [ 21 ] which has a "cell state" c t {\displaystyle c_{t}} that can ...