Batch Gradient Variance
Cross-source consensus on Batch Gradient Variance from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Benefits
Comparisons
Highlighted claims
- The final term in the sampled modified objective is the variance of mini-batch gradients and is denoted R(w). — Implicit Regularization of Mini-Batch Training in Graph Neural Networks
- Sequential per-batch SGD updates introduce a gradient-variance penalty. — Implicit Regularization of Mini-Batch Training in Graph Neural Networks
- RNS has the lowest loss variance and gradient variance among the compared samplers at random initialization. — Implicit Regularization of Mini-Batch Training in Graph Neural Networks
- The main sampler-dependent difference is batch-gradient stability and homogeneity rather than mean gradient scale. — Implicit Regularization of Mini-Batch Training in Graph Neural Networks
- Lower gradient variance is presented as beneficial because evaluation occurs on the full graph. — Implicit Regularization of Mini-Batch Training in Graph Neural Networks