ts3.models.supervised#
Models trained in-suite on the region labels, which emit probabilities, not embeddings.
They skip ts3/extract.py and the probes entirely and write their own submission, so the
inductive/transductive question does not arise: what sets them apart is supervision on the
scored label, not adaptation on the eval sessions.
ISI histogram encoder, attention pooling and a brain region classifier [Schneider et al., 2023]. |
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Multi-Head Global pooling layer. |
Trainers#
Supervised training of LOLCAT, the one TS3 model fit on the region labels. |
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Samples elements randomly from a given list of indices, without replacement. |