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linear read-out

topic1 events
papersSEP 10 04:00 UTC

Study derives high-probability guarantees for reading out superposed features in neural networks

A preprint cross-listed on arXiv's AI and machine-learning feeds investigates how networks store more concepts than they have dimensions via superposition, and how interference between stored features restricts how many can be recovered through linear read-out. By casting this recovery problem as a compressed sensing task, the authors establish conditions under which multiple simultaneously active features can be decoded with high probability.

interpretability/superposition/neural-networks/theory