Optimized Neural Network Story Generator
نویسندگان
چکیده
The paper inspired us most describes skip-thought vector algorithm to track the sentence semantics. Sentences that share semantic and syntactic properties are thus mapped to similar vector representations. Future Work • We will find a way to improve training efficiency in further and reduce computational complexity in network parameterization. • In future, instead of a simple description of the picture, we want to optimized our module to understand the meaning of a picture. Evaluation Result ht = (1− zt )⊙ ht−1 + zt ⊙ " ht " ht = g(Whxt +Uh(rt ⊙ ht−1)+ bh )
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