Search alternatives:
significant portion » significant proportion (Expand Search), significant positive (Expand Search), significant correlation (Expand Search)
portion decrease » proportional decrease (Expand Search), point decrease (Expand Search)
linear decrease » linear increase (Expand Search)
we decrease » _ decrease (Expand Search), a decrease (Expand Search), nn decrease (Expand Search)
significant portion » significant proportion (Expand Search), significant positive (Expand Search), significant correlation (Expand Search)
portion decrease » proportional decrease (Expand Search), point decrease (Expand Search)
linear decrease » linear increase (Expand Search)
we decrease » _ decrease (Expand Search), a decrease (Expand Search), nn decrease (Expand Search)
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Geometric manifold comparison visualization
Published 2025“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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Hyperparameter ranges
Published 2025“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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Convolutional vs RNN context encoder
Published 2025“…In this work, we propose to use a generative non-linear deep learning model, a disentangled variational autoencoder (DSVAE), that factorizes out window-specific (context) information from timestep-specific (local) information. …”
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The demographics questionnaire includes questions about age, education, gender and employment.
Published 2024Subjects: -
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RTs (in ms, bars) and ACC (in%, line) across time points, with 95% confidence interval.
Published 2024Subjects: -
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RTs (in ms, bars) and ACC (in %, line) for the rebound effect, with 95% confidence interval.
Published 2024Subjects: -
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The Samn-Perelli Scale is a 7-point Likert scale used to assess fatigue [42, 45].
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