Regression & Multitask#502
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…avior for the s4kernel parameters while tracking the modifications in ml4gw.nn.ssm.s4d
…kground. Updated datasets, waveform generators and models to accept/return parameters as part of the batch
…lasses. Updated Autoencoder and Supervised models to inherit from AframeClassification. Added new SupervisedMultiTaskAframe and SupervisedRegressionAframe classes for multi-task and regression tasks.
- Introduced RegressionArchitecture and MultiTaskArchitecture classes in regression.py. - Added RegressionTimeDomainResNet and MultiTaskTimeDomainResNet classes for regression tasks. - Created multitask.yaml and regression.yaml for multi-task and regression training configurations. - Enhanced BaseAframeDataset to support parameter transformations. - Implemented ChirpMass and MassRatio transforms for parameter calculations. - Updated AframeWandbLogger for improved logging capabilities.
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GaussianNLLRegressionAframe predicts a mean and variance per parameter and trains with a beta-weighted Gaussian NLL (BetaNLLLoss); detection score is the negative mean variance. Includes an example S4D config.
Drop the removed length/prenorm/lr arguments and pass d_state.
Read metadata and per-rank slices via h5py/_load_with_idx instead of loading whole ledgers into memory (WaveformLoader init and WaveformSampler val), add a num_val_waveforms cap, and update the sampler test mock accordingly.
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Mirroring #452 by pushing into
regression-datamodulesbranch.