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Deep Models for Engagement Assessment With Scarce Label Information

Deep Models for Engagement Assessment With Scarce Label Information

21 October 2016
Feng Li
Guangfan Zhang
Wei Wang
R. Xu
Tom Schnell
Jonathan Wen
F. McKenzie
Jiang Li
ArXiv (abs)PDFHTML

Papers citing "Deep Models for Engagement Assessment With Scarce Label Information"

5 / 5 papers shown
Title
Guided Self-attention: Find the Generalized Necessarily Distinct Vectors
  for Grain Size Grading
Guided Self-attention: Find the Generalized Necessarily Distinct Vectors for Grain Size Grading
Fang Gao
XueTao Li
Jiabao Wang
Shengheng Ma
Jun Yu
48
0
0
08 Oct 2024
Systematic Review of Experimental Paradigms and Deep Neural Networks for
  Electroencephalography-Based Cognitive Workload Detection
Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection
K. Vishnu
C. N. Gupta
43
2
0
11 Sep 2023
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
Deep Learning in EEG: Advance of the Last Ten-Year Critical Period
Shu Gong
Kaibo Xing
A. Cichocki
Junhua Li
VLM
113
68
0
22 Nov 2020
Learning from Heterogeneous EEG Signals with Differentiable Channel
  Reordering
Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering
Aaqib Saeed
David Grangier
Olivier Pietquin
Neil Zeghidour
77
21
0
21 Oct 2020
Deep learning-based electroencephalography analysis: a systematic review
Deep learning-based electroencephalography analysis: a systematic review
Yannick Roy
Hubert J. Banville
Isabela Albuquerque
Alexandre Gramfort
T. Falk
J. Faubert
150
978
0
16 Jan 2019
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