Parameter Learning of Sum-Product Networks

@Author: Han Zhao

@Note: Please cite the following papers if you use the tool developed in this package.

A Unified Approach for Learning the Parameters of Sum-Product Networks by H. Zhao, P. Poupart and G. Gordon, NIPS 2016.

Collapsed Variational Inference for Sum-Product Networks by H. Zhao, T. Adel, G. Gordon and B. Amos, ICML 2016.

Linear Time Computation of Moments in Sum-Product Networks by H. Zhao and G. Gordon, NIPS 2017.

@Required lib:

• Boost (>= 1.55)
• CMake (>= 2.80)

---

The software is written in C++11 for the purpose of parameter learning in Sum-Product Networks. It supports batch learning, online learning as well as streaming learning. This software implements the following learning algorithms for SPNs:

1. Projected Gradient Descent.
2. Exponentiated Gradient Method.
3. Sequential Monomial Approximation (Zhao et al., NIPS 2016).
4. Concave-Convex Procedure/Expectation Maximization (Zhao et al., NIPS 2016).
5. Collapsed Variational Inference (Zhao et al., ICML 2016).
6. Online Bayesian Moment Matching (Rashwan et al., AISTATS 2016)
7. Assumed Density Filtering (Zhao et al., NIPS 2017)

---

Usage:

1. batch_learning.cpp, online_learning.cpp and stream_learning.cpp should be the starting source files in order to understand the package.
2. All the code about network structures is implemented in SPNNode.[h|cpp] and SPNetwork.[h|cpp].
3. Learning algorithms under different learning scenarios, e.g., batch, online and streaming, are implemented separately in the corresponding *.cpp files.