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Liquid neural networks can enhance aircraft engine health monitoring by disentangling degradation from operating conditions, improving forecasting accuracy and interpretability.
Logical visual anomalies can be detected with 10% higher accuracy by modeling spatial relations rather than relying solely on local features.
Medical VQA models can now reason more reliably thanks to a new framework that disentangles true causal effects from spurious correlations by jointly tackling observable and unobservable confounders.
Finally, a deep learning model for AKI prediction that doesn't just predict, but tells you *why*, by tracing the causal chain of physiological events.
