Release 0.2.2 (#110)

Author: StannisZhou

README.md

@@ -6,11 +6,14 @@
 
 # PGMax
 
-PGMax implements general factor graphs for probabilistic graphical models (PGMs) with discrete variables, and hardware-accelerated differentiable loopy belief propagation (LBP) in [JAX](https://jax.readthedocs.io/en/latest/).
+PGMax implements general factor graphs for discrete probabilistic graphical models (PGMs), and hardware-accelerated differentiable loopy belief propagation (LBP) in [JAX](https://jax.readthedocs.io/en/latest/).
 
-- **General factor graphs**: PGMax goes beyond pairwise PGMs, and supports arbitrary factor graph topology, including higher-order factors.
+- **General factor graphs**: PGMax supports easy specification of general factor graphs with potentially complicated topology, factor definitions, and discrete variables with a varying number of states.
 - **LBP in JAX**: PGMax generates pure JAX functions implementing LBP for a given factor graph. The generated pure JAX functions run on modern accelerators (GPU/TPU), work with JAX transformations (e.g. `vmap` for processing batches of models/samples, `grad` for differentiating through the LBP iterative process), and can be easily used as part of a larger end-to-end differentiable system.
 
+[**Installation**](#installation)
+| [**Getting started**](#getting-started)
+
 ## Installation
 
 ### Install from PyPI
@@ -37,6 +40,16 @@ pre-commit install
 
 By default the above commands install JAX for CPU. If you have access to a GPU, follow the official instructions [here](https://github.com/google/jax#pip-installation-gpu-cuda) to install JAX for GPU.
 
+## Getting Started
+
+Here are a few self-contained Colab notebooks to help you get started on using PGMax:
+
+- [Tutorial on basic PGMax usage](https://colab.research.google.com/drive/1PQ9eVaOg336XzPqko-v_us3izEbjvWMW?usp=sharing)
+- [Implementing max-product LBP](https://colab.research.google.com/drive/1mSffrA1WgQwgIiJQd2pLULPa5YKAOJOX?usp=sharing) for [Recursive Cortical Networks](https://www.science.org/doi/10.1126/science.aag2612)
+- [End-to-end differentiable LBP for gradient-based PGM training](https://colab.research.google.com/drive/1yxDCLwhX0PVgFS7NHUcXG3ptMAY1CxMC?usp=sharing)
+
+
+
 ## Citing PGMax
 
 To cite this repository
@@ -45,8 +58,8 @@ To cite this repository
   author = {Guangyao Zhou* and Nishanth Kumar* and Miguel L\’{a}zaro-Gredilla and Dileep George},
   title = {{PGMax}: {F}actor graph on discrete variables and hardware-accelerated differentiable loopy belief propagation in {JAX}},
   howpublished={\url{http://github.com/vicariousinc/PGMax}},
-  version = {0.2.1},
-  year = {2021},
+  version = {0.2.2},
+  year = {2022},
 }
 ```
 where * indicates equal contribution.

docs/source/conf.py

@@ -26,7 +26,7 @@
 )
 
 # The full version, including alpha/beta/rc tags
-release = "0.2.1"
+release = "0.2.2"
 
 
 # -- General configuration ---------------------------------------------------