بدائل البحث:
significantly weaker » significantly greater (توسيع البحث), significantly better (توسيع البحث), significantly related (توسيع البحث)
linear decrease » linear increase (توسيع البحث)
weaker decrease » greater decrease (توسيع البحث), teer decrease (توسيع البحث), water decreases (توسيع البحث)
we decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), nn decrease (توسيع البحث)
significantly weaker » significantly greater (توسيع البحث), significantly better (توسيع البحث), significantly related (توسيع البحث)
linear decrease » linear increase (توسيع البحث)
weaker decrease » greater decrease (توسيع البحث), teer decrease (توسيع البحث), water decreases (توسيع البحث)
we decrease » _ decrease (توسيع البحث), a decrease (توسيع البحث), nn decrease (توسيع البحث)
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Detailed information of the observation datasets.
منشور في 2025"…Understanding spatial-temporal characteristics of wind speed is significant in meteorology, coastal engineering design and maritime industries. …"
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General technical specification for GW154/6700.
منشور في 2025"…Understanding spatial-temporal characteristics of wind speed is significant in meteorology, coastal engineering design and maritime industries. …"
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Geometric manifold comparison visualization
منشور في 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
منشور في 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
منشور في 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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