Replacing activation-function derivatives with constant or random stand-ins trains small neural nets, but the paper's proof that gradient direction is unaffected is flawed.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.NE 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions
Replacing activation-function derivatives with constant or random stand-ins trains small neural nets, but the paper's proof that gradient direction is unaffected is flawed.