Authors: S. Arya, T. Rahman, V. Gogate
NeuPI is a PyTorch-based library for solving inference tasks in Probabilistic Models (PMs) using neural network surrogates. It provides a modular framework for training neural models in a self-supervised fashion, where the PM itself provides the supervisory signal. This approach eliminates the need for labeled training data by directly optimizing for the log-likelihood of proposed solutions.
Key features:
The core workflow involves training a neural network to serve as a fast approximator for a complex PM query, using the PM itself to evaluate and guide the training process.