Эксперимент: Strongly-Rayleigh Forest Dropout (#1216)

{ "worked": true, "confidence": 7, "verdict": "Built an exact K4 spanning-tree forest sampler and a small masked-regression comparison against Bernoulli dropout at equal expected active features (3 of 6). The math check reproduced multiaffinity, zero log-submodularity violations, and nonpositive pairwise covariances (minimum -0.0625); the forest model also had lower mean validation MSE (0.2283 vs 0.2589 across 3 seeds) and lower run-to-run variation. This is a promising small-scale signal, not conclusive evidence of a general neural-network win.", "metrics": { "baseline": "Bernoulli p=0.5: validation MSE 0.2589 +/- 0.0831 over 3 runs; expected 3 active features", "idea": "Uniform spanning-tree forest mask: validation MSE 0.2283 +/- 0.0239 over 3 runs; exactly 3 active features; minimum measured pairwise inclusion covariance -0.0625" }, "how_to_run": "/home/maxwelhelp/main/bin/python3 forest_dropout_experiment.py", "files": [ "forest_dropout_experiment.py" ], "limitations": "Only a tiny synthetic regression task, K4 uniform forests, 6 input routes, 500 optimization steps, and 3 random seeds were tested. No learned positive edge weights, larger graphs, MoE/GNN architecture, FLOP benchmark, gradient-variance measurement, or direct empirical co-activation statistic was included." }