AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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It is argued that intervention-based robustness tests should become standard for trustworthy clinical speech models and show that high predictive performance and the absence of a significant diagnostic-group difference in a measured acoustic factor are not sufficient for robustness.
A new model family (CortexMAE) trained using the masked autoencoder framework is introduced, and in this setting the CortexMAE family outperforms prior models by a large margin, and the first open evaluation suite (Brainmarks) for fMRI foundation models is released.
The findings suggest that robust, individualized brain-based predictions for mental health outcomes remain challenging with the available data and sample size.
A layer-wise analysis of nine SSL speech backbones using a low-capacity logistic regression probe reveals that the transferred discriminative signal lacks pathological specificity, highlighting critical limitations that must be addressed before speech-based pathology recognition models can be reliably deployed in clini...
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