Recurrent Neural Networks
Cross-source consensus on Recurrent Neural Networks from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Risks & contraindications
Comparisons
Highlighted claims
- For recurrent ReLU networks, integer threshold gates are exactly implemented by the difference of two ReLU terms. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- Bounded-range admissible activation networks must use encoded inputs because their state space is compact. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- Admissible bounded activations require primitive recursive computable-analysis evaluation, a modulus of continuity, and a detector separating low and high input ranges. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- Recurrent ReLU computation iterates a fixed feedforward ReLU block from raw integer input and reads exact output after a primitive recursive observation time. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs
- ReLU networks and admissible-ρ networks compute only primitive recursive functions under the stated bounds. — Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs