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Phase 2 — Pinning + History: - snapshot-stack.js: ring buffer (20 max) with auto-snapshot on train/randomize/thumbs-down, multi-level undo, tagged entries - ab-compare.js: A/B weight state comparison with capture/toggle/accept/revert - region-pin.js: pin rectangular input-space regions (Approach A: example pinning), pinned examples always included in training - param-pin.js: per-output pin flags, pin mask skips pinned nodes in moveWeights - phase2-ui.js: undo button with history popup, A/B toggle, long-press region pin, double-tap param pin - Modified mlp.js/iml.js/nisps-wasm.js to accept outputPinMask in moveWeights Phase 3 — Input Refinement + Exploration: - pressure-feedback.js: touch force + hold duration → intensity multiplier - auto-explore.js: automated thumbs-down at configurable interval, zoom-scaled - input-heatmap.js: 16×16 MLP sampling, 3 color modes (luminance/variance/ divergence), zoom-aware resampling, offscreen canvas rendering - phase3-ui.js: auto-explore toggle with progress ring, heatmap eye icon, pressure indicators, settings drawer section - joy-map-enhanced.js: added setHeatmap() for background layer rendering Phase 4 — Output Pipeline + Visualization + Polish: - output-pipeline.js: global curve → smoothing → slew rate → freeze gate - weight-health.js: weight magnitude histogram, dead/saturating/healthy status - gradient-flow.js: per-layer weight-delta analysis, vanishing/exploding detection - session-presets.js: save/load full state, URL sharing via compact params - phase4-ui.js: freeze button, network health panel, session preset UI All phases merged into a-app.js with proper integration: auto-snapshots, pressure-modulated RL, heatmap triggers, output pipeline in routeOutputs, gradient capture around training, persistence for all new state. |
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|---|---|---|
| .beads | ||
| .github/workflows | ||
| .vscode | ||
| data | ||
| modes | ||
| nisps-core | ||
| playground | ||
| src | ||
| vcv | ||
| 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.