<b>Ground motion modeling and Epistemic Uncertainty using Conditional Variational Autoencoders</b> by Ravi Kanth Sriwastav (18279283)

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Encoder with input concatenation . by Imad Eddine Tibermacine (22537816)

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Results obtained on the test set. by Imad Eddine Tibermacine (22537816)

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Results obtained on the training set. by Imad Eddine Tibermacine (22537816)

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End-to-end pipeline summary (data, preprocessing, features, and model). by Imad Eddine Tibermacine (22537816)

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CVAE architecture and training hyperparameters. by Imad Eddine Tibermacine (22537816)

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Baseline models and hyperparameters under the harmonised pipeline. by Imad Eddine Tibermacine (22537816)

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Per-channel features and dimensionalities (per 1 s window). by Imad Eddine Tibermacine (22537816)

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Retrained baselines under the harmonized pipeline (identical preprocessing; stratified 80/20 split). by Imad Eddine Tibermacine (22537816)

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Uncertainty and significance on the identical test set ( windows). by Imad Eddine Tibermacine (22537816)

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Training curves (losses and accuracies) with and showing rapid convergence and stable generalization. by Imad Eddine Tibermacine (22537816)

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Illustrative 1 s time-domain visualization over 11 channels based on feature-space reconstructions (blue) for an unhealthy segment (green) and a generated sample from (orange). by Imad Eddine Tibermacine (22537816)

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Overall architecture of the proposed method (feature-space CVAE). by Imad Eddine Tibermacine (22537816)

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Decoder with latent concatenation , implementing . by Imad Eddine Tibermacine (22537816)

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Illustrative time-domain visualization derived post hoc from feature-space reconstruction (blue) for a 125 Hz unhealthy segment (green). by Imad Eddine Tibermacine (22537816)

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Precision, recall and F1-score obtained on the test set. by Imad Eddine Tibermacine (22537816)

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Confusion matrices for the Motor Task/Channel filtering experiment: training (top) and test (bottom). by Imad Eddine Tibermacine (22537816)

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Component B relates educational and behavioral outcomes shaped by one’s upbringing. by Justin Marotta (22049550)

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S1 Fig. The first 10 PCA components capture a high proportion of variance in the ABCD Study®. by Justin Marotta (22049550)

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Evaluated CVAE architectures. Each architecture was trained with different randomized model weight initializations across 25 instances to compute the mean MSE reconstruction error. by Justin Marotta (22049550)

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