InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy

2019/08/29 by Cózar, Javier, Cabañas, Rafael, Salmerón, Antonio +1
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1908.11161

Abstract

InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other libraries whose focus is on dealing with very general probabilistic models at the cost of having a more complex API. In particular, this package allows to define, learn and evaluate general hierarchical probabilistic models containing deep neural networks in a compact and simple way. InferPy is built on top of Tensorflow Probability and Keras.

Related

• Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models
• SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning\n using Sum-Product Networks
• Borch: A Deep Universal Probabilistic Programming Language
• Pyro: Deep Universal Probabilistic Programming
• py/cuTAGI: An Open-Source Library for Tractable Approximate Gaussian Inference in Bayesian Neural Networks