No description
3db90b035a
Add ?spread=0-1 URL param that controls weight initialization scaling, RL noise scaling per layer, noise cap, and weight decay to prevent sigmoid output saturation. At spread=0 (original behavior) weights are uniform [-1,1] and outputs polarise near 0/1. At spread=1 weights use Xavier scaling (1/sqrt(fan_in)), noise is proportionally reduced, and 10% weight decay per thumbs-down prevents unbounded magnitude drift. Also fix randomise to re-inject current joystick position and re-run inference before routing outputs, eliminating the jump on first joystick move after randomise. Defaults: tame=1, spread=0.6 across all app variants. |
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|---|---|---|
| .beads | ||
| .vscode | ||
| data | ||
| modes | ||
| nisps-core | ||
| playground | ||
| src | ||
| voicespaces | ||
| .gitignore | ||
| .gitmodules | ||
| AGENTS.md | ||
| ChannelStripAudioApp.hpp | ||
| CLAUDE.md | ||
| IMLInterface.hpp | ||
| LICENSE | ||
| MEMLNaut-NISPS.ino | ||
| NISPS_CORE_EXTRACTION_PLAN.md | ||
| NISPS_CORE_TASKS.md | ||
| PAFSynthAudioApp.hpp | ||
| README.md | ||
| ThruAudioApp.hpp | ||
| XiasriAnalysis.cpp | ||
| XiasriAnalysis.hpp | ||
| XIASRIAudioApp.hpp | ||
Neural Interactive Shaping of Parameter Spaces
https://musicallyembodiedml.github.io/memlnaut/approaches/nisps
Web Playground
Try NISPS in your browser — no hardware required:
cd playground
python3 -m http.server
# Open http://localhost:8000
Train a neural network to map joystick positions to generative visuals through interactive machine learning. Two learning modes: direct example mapping and reinforcement learning with thumbs up/down feedback.
The playground UI includes an Expand toggle on the visual surface so you can make the canvas nearly full-screen while compressing parameter/control panels into a minimal strip beneath it.