Training ideas

Curricula, sampling schedules, pseudo-labeling and multi-task mixing, with the standard training recipe as the control.

2 ideas found

Status: Beats tuned baseline Category: Training Math: Spectral theory Clear all

✓✓ Beats tuned baseline 2026

Laplacian-Coherence Graph Minibatches

Replace uniform node minibatches in a GNN with a coreset selected from a small random candidate set using local Laplacian-column coherence. Select nodes whose connectivity signatures are least redundant with already selected nodes, while retaining inverse-probability weights for unbiased loss estimates. This should improve coverage of weakly connected graph clusters and preserve smooth graph signals at the same batch size.

Useful7/10

Difficulty5/10

Novelty7/10

Training Sample efficiency Scalability Accuracy

Paper: Scalable Graph Coreset Selection via Greedy Sampling arXiv:2607.27602

✓✓ Beats tuned baseline 2026

Spectral-Width Prethermal Training Schedule

Use the paper's lifetime law as a controller for training or rollout difficulty. Estimate the active perturbation bandwidth R of hidden states or forecast errors and reduce the residual gain, increase the dispersion order W, or inject controlled bandwidth whenever the estimated prethermal lifetime becomes too short.

Useful7/10

Difficulty5/10

Novelty8/10

Training Stability Speedup Generalization

Paper: From stable periodic orbits to many-body chaos: doubly tunable prethermalization via engineering of an emergent band structure arXiv:2607.12355