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| <div class="eyebrow">Jacob Garcia · Hugging Face Model Foundry</div> |
| <h1>Neural Process Pocket Lab</h1> |
| <p class="lead">Interactive few-shot function distribution inference. This showcase backs up the |
| trained artifacts, measured evaluation, and complete runnable source.</p> |
| <div class="actions"> |
| <a class="button" href="https://huggingface.co/spaces/ARotting/neural-process-pocket-lab/tree/main">Explore every file</a> |
| <a class="button alt" href="https://huggingface.co/ARotting">View the full foundry</a> |
| </div> |
| <div class="grid"> |
| <section class="card"> |
| <h2>Verified project card</h2> |
| <pre># 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 |
| ``` |
| </pre> |
| <h2>Evaluation snapshot</h2> |
| <pre>{ |
| "model": "Neural Process Pocket", |
| "parameters": 12866, |
| "best_step": 5000, |
| "benchmark": { |
| "conditional_neural_process": { |
| "rmse": 1.092705488204956, |
| "gaussian_nll": 1.241895318031311, |
| "coverage_90": 0.8876199722290039 |
| }, |
| "fixed_rbf_gaussian_process": { |
| "rmse": 1.2585044517936235, |
| "gaussian_nll": 1.3279816679794703, |
| "coverage_90": 0.73632 |
| }, |
| "tasks": 500, |
| "context_points_per_task": 5, |
| "targets_per_task": 100 |
| } |
| }</pre> |
| </section> |
| <section class="card"> |
| <h2>Backed-up artifact tree</h2> |
| <input id="filter" placeholder="Filter files…" autocomplete="off"> |
| <ul id="files"><li><code>README.md</code></li> |
| <li><code>__pycache__/app.cpython-311.pyc</code></li> |
| <li><code>__pycache__/model.cpython-311.pyc</code></li> |
| <li><code>app.py</code></li> |
| <li><code>artifacts/neural-process-pocket/evaluation.json</code></li> |
| <li><code>artifacts/neural-process-pocket/model.safetensors</code></li> |
| <li><code>data/heldout_tasks.parquet</code></li> |
| <li><code>model.py</code></li> |
| <li><code>requirements.txt</code></li> |
| <li><code>train.py</code></li></ul> |
| </section> |
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