Number of RNS Parts
Cross-source consensus on Number of RNS Parts from 1 sources and 4 claims.
1 sources · 4 claims
How it works
Dosage & preparation
Risks & contraindications
Highlighted claims
- The number of RNS batches per epoch strongly affects accuracy even when other training hyperparameters are independently tuned. — Implicit Regularization of Mini-Batch Training in Graph Neural Networks
- The paper recommends starting with m = 2 and increasing it until accuracy plateaus. — Implicit Regularization of Mini-Batch Training in Graph Neural Networks
- An RNS epoch processes about |E|/m induced edges across all batches. — Implicit Regularization of Mini-Batch Training in Graph Neural Networks
- Increasing m can damage topology by increasing diameter or removing too much connectivity. — Implicit Regularization of Mini-Batch Training in Graph Neural Networks