This is the Linux app named The Neural Process Family whose latest release can be downloaded as neural-processessourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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The Neural Process Family
DESCRIPTION:
Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation. They can learn distributions over functions from data and efficiently make predictions at new inputs with calibrated uncertainty — making them useful for few-shot learning, Bayesian regression, and meta-learning. Each notebook includes theoretical explanations, key building blocks, and executable code that runs directly in Google Colab, requiring no local setup. Implementations rely only on standard dependencies such as NumPy, TensorFlow, and Matplotlib, and provide visualizations of model performance.
Features
- No installation required when run in Google Colab
- Ideal for educational use, research prototyping, and experimentation
- Includes visualization tools for function reconstruction and prediction
- Lightweight TensorFlow implementation with minimal dependencies
- Demonstrates meta-learning and uncertainty-aware regression
- Interactive Colab notebooks for CNP, NP, and ANP models
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/neural-process-family.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.