| title: Neural Process Pocket | |
| emoji: 🌊 | |
| colorFrom: violet | |
| colorTo: cyan | |
| sdk: gradio | |
| sdk_version: "6.5.1" | |
| app_file: app.py | |
| pinned: false | |
| # Neural Process Pocket | |
| Neural Process Pocket is a Conditional Neural Process trained across a | |
| distribution of sine functions. Five unordered context observations are encoded | |
| into a task representation; a probabilistic decoder predicts the mean and | |
| standard deviation at arbitrary target coordinates. | |
| Evaluation covers RMSE, Gaussian negative log likelihood, and empirical 90% | |
| interval coverage on unseen functions. A fixed-kernel RBF Gaussian Process is the | |
| non-neural few-shot control. | |
| ## Verified local result | |
| The 12,866-parameter CNP reached 1.093 RMSE, 1.242 Gaussian NLL, and 88.76% | |
| coverage for nominal 90% intervals across 500 unseen five-context-point tasks. | |
| The fixed-kernel Gaussian Process reached 1.259 RMSE, 1.328 NLL, and 73.63% | |
| coverage. | |
| ```bash | |
| uv run python projects/neural-process-pocket/train.py | |
| uv run pytest tests/test_neural_process_pocket.py | |
| ``` | |