mask-ddpm/docs/ideas.md

Ideas & Hypotheses

Transformer as backbone (Plan B)

• Hypothesis: self-attention may better capture long-range dependencies and reduce conflict between temporal consistency and distribution matching.
• Risk: higher compute cost, potentially more unstable training.
• Status: implemented as backbone_type = "transformer" in config.
• Experiment: compare GRU vs Transformer using run_compare.py.

Residual standardization

• Hypothesis: standardizing residuals before diffusion reduces drift and improves KS.

Two-stage training with curriculum

• Hypothesis: train diffusion on residuals only after temporal GRU converges to low error.

Discrete calibration

• Hypothesis: post-hoc calibration on discrete marginals can reduce JSD without harming KS.

Feature-type split modeling

• Hypothesis: separate generation per feature type (setpoints, controllers, actuators, quantized, derived, aux) yields better overall fidelity.