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Quantifying stimulus-relevant representational drift using cross-modality contrastive learning
The representational drift observed in neural populations raises serious questions about how accurate decoding survives these changes.
Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method
We demonstrate that neural collapse leads to good generalization specifically when it approaches an optimal IB solution of the classification problem.
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