Speeding up deep neural network training with decoupled and analytic learning
Training deep neural networks usually demands a significantly long period of time. In this thesis, we explore methods in two different areas, i.e., decoupled learning and analytic learning, in order to reduce the training time. In decoupled learning, new methods are proposed to alleviate the sequ...
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格式: | Thesis-Doctor of Philosophy |
語言: | English |
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Nanyang Technological University
2021
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在線閱讀: | https://hdl.handle.net/10356/153079 |
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機構: | Nanyang Technological University |
語言: | English |