AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes MAPA, an otherwise vanilla masked autoencoder with two spatial encodings, an anatomical region embedding and a relative positional encoding that together enable it to learn neural representations that transfer to unseen subjects and across various tasks.
By aligning patient-specific neural data to a shared latent space, it is shown that speech BCIs can be trained on data combined across patients, enabling cross-patient speech BCIs and support future speech BCIs that are more accurate and rapidly deployable.
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