Attentional control methods for time-series data classification and synthesis = 시계열 데이터 분류와 합성을 위한 주의집중조절 방법

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Development of attention modules has improved the efficiency of neural networks by selectively encoding contextual information for inputs and outputs, regardless of size, length, or condition. Some recent studies have reported that successful control of attention increases classification performance for both training and test datasets. Building on these recent insights, this paper proposes two novel methods of attentional control for efficient processing of time-series data: a reinforcement learning (RL)-based attentional control algorithm that selects appropriate modular models according to contextual changes over time and a method for regularizing attentional control by embedding a novel alignment loss in causal sequence-to-sequence problems. Each attentional control method was tested on two such problems: EEG cognitive load classification and speech synthesis. The results confirm that these models outperform conventional methods.
Advisors
Lee, Sang Wanresearcher이상완researcher
Description
한국과학기술원 :바이오및뇌공학과,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 바이오및뇌공학과, 2019.2,[v, 60 p. :]

Keywords

Attentional control▼adeep neural network▼atime-series learning▼aEEG classification▼aspeech synthesis; 주의집중조절▼a심층 신경망▼a시계열 학습▼aEEG 신호 분류▼a음성 합성

URI
http://hdl.handle.net/10203/285199
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=947923&flag=dissertation
Appears in Collection
BiS-Theses_Master(석사논문)
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