Signal-to-Noise Ratio and Sample Size Govern Representational Alignment in Neural Networks
Neural networks are known to develop latent representations that are $aligned$, namely structurally similar across networks trained with different architectures, training protocols, or training datasets. We study this phenomenon in a controlled setting, where we train an ensemble of networks on regression and classification tasks using training sets perturbed by independent realizations of a noise process. We show that the signal-to-noise ratio (SNR) and the training sample size influence the alignment in qualitatively similar ways in networks trained on real-world datasets and in an extremely simple $linear$ network with a single hidden layer, for which the alignment can be estimated ana...
| Authors | Ali Hussaini Umar, Alessandro Laio |
|---|---|
| Primary category | stat.ML |
| Published | 26 May 2026 |
| Last updated at source | 26 May 2026 |
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