A pipeline using discrete VAE compression of CSI data followed by causal discovery and LTL rule extraction yields a symbolic, causally interpretable classifier for human activity recognition with competitive performance.
Beta-VAE: Learning basic visual concepts with a constrained variational framework
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Towards Causally Interpretable Wi-Fi CSI-Based Human Activity Recognition with Discrete Latent Compression and LTL Rule Extraction
A pipeline using discrete VAE compression of CSI data followed by causal discovery and LTL rule extraction yields a symbolic, causally interpretable classifier for human activity recognition with competitive performance.