Training ideas

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

2 ideas found

Category: Training Solves: Scalability ML: Rnn Clear all

△ Mechanism confirmed, baseline not beaten 2026

Lyapunov-Calibrated Multiplicative Noise

Use measured local Jacobian growth to set the variance of dropout, feature noise, or stochastic-depth perturbations, implementing the paper's fluctuation-response idea that multiplicative noise is tied to the positive scrambling or Lyapunov rate. The controller maintains a target growth regime instead of applying a fixed noise schedule throughout training. It predicts a stability transition when the estimated growth rate crosses zero and a variance-growth proportionality that can be tested…

Useful7/10

Difficulty4/10

Novelty6/10

Training Stability Generalization Scalability

Paper: Effective Field Theory of Operator Scrambling from Strong-to-Weak Symmetry Breaking arXiv:2607.24925

△ Mechanism confirmed, baseline not beaten 2026

Adaptive Ballistic-to-Diffusive Propagation Schedule

Use dephasing as a depth- or time-dependent control variable rather than a fixed regularizer: early layers retain coherent transport for feature discrimination, while later layers increase dephasing to eliminate unstable high-frequency oscillations. The schedule is selected from an observable spectral or correlation ratio, giving a falsifiable switch point instead of tuning noise blindly.

Useful7/10

Difficulty5/10

Novelty8/10

Training Stability Accuracy Scalability

Paper: Fermions on a 1D lattice: localized sources and sinks with dephasing arXiv:2607.22240