FACT is a first-order stationarity identity for weight matrices that matches or beats the Neural Feature Ansatz as a description of learned features at convergence.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations
FACT is a first-order stationarity identity for weight matrices that matches or beats the Neural Feature Ansatz as a description of learned features at convergence.