Learning Simpler Language Models with the Differential State Framework
نویسندگان
چکیده
منابع مشابه
Learning Simpler Language Models with the Differential State Framework
Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks such as language modeling. Existing architectures that address the issue are often complex and costly to train. The differential state framework (DSF) is a simple and high-performing design that unifies previously introduced gated neural models. DSF models maintain longer-te...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 2017
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco_a_01017