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Improving Performance of Recurrent Neural Networks

Student: Valeriya Ignatovskaya

Supervisor: Ilya Schurov

Faculty: Faculty of Mathematics

Educational Programme: Mathematics (Bachelor)

Year of Graduation: 2018

The paper deals with the problem of exploding and vanishing gradients during the training of recurrent neural networks. Several existing approaches to solution of this problem are described. In particular, unitary recurrent neural networks and ways to implement them are thoroughly explored. In addition, own idea of implementing a unitary recurrent neural network is being developed. The effectiveness of each method of implementation is experimentally demonstrated on topical problems from different areas.

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