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Non-Convex Optimization: RMSProp Based Optimization for Long Short-Term Memory Network
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2020-05
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Final Thesis Submission
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Research Projects
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Abstract
This project would give a comprehensive picture of non-convex optimization for deep
learning, explain in details about Long Short-Term Memory (LSTM) and RMSProp. We
start by illustrating the internal mechanisms of LSTM, like the network structure and
backpropagation through time (BPTT). Then introducing RMSProp optimization, some
relevant mathematical theorems and proofs in those sections, which give a clear picture of
how RMSProp algorithm is helpful to escape the saddle point. After all the above, we apply
it with LSTM with RMSProp for the experiment; the result would present the efficiency and
accuracy, especially how our method beat traditional strategy in non-convex optimization.
