A structured list of resources about Sum-Product Networks (SPNs)
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README.md Updating Butz2018b to PGM 2018 Sep 23, 2018

README.md

Awesome Sum-Product Networks

awesome-spn is a curated and structured list of resources about Sum-Product Networks (SPNs), tractable deep density estimators.

This includes (even not formally published) research papers sorted by year and topics as well as links to tutorials and code and other related Tractable Probabilistic Models (TPMs). It is inspired by the SPN page at the Washington University.

Licence and Contributing

CC0

awesome-spn is released under Public Domain. Feel free to complete and/or correct any of these lists. Pull requests are very welcome!

Table of Contents

Papers

Sorted by year or topics

Year

2018

2017

2016

2015

2014

2013

2012

2011

Topics

Weight Learning

Structure Learning

Representation Learning

Modeling

Applications

Theory

Related Works

Arithmetic Circuits

Other TPMs

Exploiting Sum-Product Theorem

Resources

Dataset

Code

Talks and Tutorials

References

[Adel2015]
Adel, Tameem and Balduzzi, David and Ghodsi, Ali
Learning the Structure of Sum-Product Networks via an SVD-based Algorithm
Uncertainty in Artificial Intelligence 2015

[Amer2012]
Amer, Mohamed and Todorovic, Sinisa
Sum-Product Networks for Modeling Activities with Stochastic Structure
2012 IEEE Conference on CVPR

[Amer2015]
Amer, Mohamed and Todorovic, Sinisa
Sum Product Networks for Activity Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence

[Bueff2018]
Bueff, Andreas and Spelchert, Stefanie and Belle, Vaishak
Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks
preprint

[Butz2018a]
Butz, Cory J. and dos Santos André E. and Oliveira Jhonatan S. and Stavrinides John
Efficient Examination of Soil Bacteria Using Probabilistic Graphical Models
International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems 2018

[Butz2018b]
Butz, Cory J. and Oliveira Jhonatan S. and dos Santos André E., Teixeira, A. L. and Poupart, P. and Kalra, A.
An Empirical Study of Methods for SPN Learning and Inference
PGM 2018

[Cheng2014]
Cheng, Wei-Chen and Kok, Stanley and Pham, Hoai Vu and Chieu, Hai Leong and Chai, Kian Ming Adam
Language modeling with Sum-Product Networks
INTERSPEECH 2014

[Choi2017]
Cheng, Arthur and Darwiche, Adnan
On Relaxing Determinism in Arithmetic Circuits
ICML 2017

[Conaty2017]
Conaty, Diarmaid and Deratani Mauá, Denis and de Campos, Cassio P.
Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks
UAI 2017

[Conaty2018]
Conaty, Diarmaid and Del Rincon, Jesus Martinez and de Campos, Cassio P.
Cascading Sum-Product Networks using Robustness
PGM 2018

[Darwiche2003]
Darwiche, Adnan
A Differential Approach to Inference in Bayesian Networks
Journal of the ACM 2003

[Dellaleau2011]
Delalleau, Olivier and Bengio, Yoshua
Shallow vs. Deep Sum-Product Networks
Advances in Neural Information Processing Systems 2011

[Dennis2012]
Dennis, Aaron and Ventura, Dan
Learning the Architecture of Sum-Product Networks Using Clustering on Varibles
Advances in Neural Information Processing Systems 25

[Dennis2015]
Dennis, Aaron and Ventura, Dan
Greedy Structure Search for Sum-product Networks
International Joint Conference on Artificial Intelligence 2015

[Dennis2017a]
Dennis, Aaron and Ventura, Dan
Online Structure-Search for Sum-Product Networks
16th IEEE International Conference on Machine Learning and Applications (ICMLA) 2017

[Dennis2017b]
Dennis, Aaron and Ventura, Dan
Autoencoder-Enhanced Sum-Product Networks
16th IEEE International Conference on Machine Learning and Applications (ICMLA) 2017

[Desana2016]
Desana, Mattia and Schn{"{o}}rr Christoph
Learning Arbitrary Sum-Product Network Leaves with Expectation-Maximization
arxiv.org/abs/1604.07243

  • [Desana2017]

Desana, Mattia and Schn{"{o}}rr Christoph
Sum-Product Graphical Models
arxiv.org/abs/1708.06438

  • [DiMauro2017]

Di Mauro, Nicola and Esposito, Floriana and Ventola, Fabrizio Giuseppe and Vergari, Antonio
Alternative variable splitting methods to learn Sum-Product Networks
Proceedings of the 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017)

  • [Friesen2015]

Friesen, Abram L. and Domingos, Pedro
Recursive Decomposition for Nonconvex Optimization
Proceedings of the 24th International Joint Conference on Artificial Intelligence

  • [Friesen2016]

Friesen, Abram L. and Domingos, Pedro
The Sum-Product Theorem: A Foundation for Learning Tractable Models
ICML 2016

  • [Friesen2017]

Friesen, Abram L. and Domingos, Pedro
Unifying Sum-Product Networks and Submodular Fields
Principled Approaches to Deep Learning Workshop at ICML 2017

  • [Gens2012]

Gens, Robert and Domingos, Pedro
Discriminative Learning of Sum-Product Networks
NIPS 2012

[Gens2013]
Gens, Robert and Domingos, Pedro
Learning the Structure of Sum-Product Networks
ICML 2013

[Gens2017]
Gens, Robert and Domingos, Pedro
Compositional Kernel Machines
ICLR 2017 - Workshop Track

[Hsu2017]
Hsu, Wilson and Kalra, Agastya and Poupart, Pascal
Online Structure Learning for Sum-Product Networks with Gaussian Leaves
ICLR 2017 - Workshop Track

[Jaini2016]
Jaini, Priyank and Rashwan, Abdullah and Zhao, Han and Liu, Yue and Banijamali, Ershad and Chen, Zhitang and Poupart, Pascal
Online Algorithms for Sum-Product Networks with Continuous Variables
International Conference on Probabilistic Graphical Models 2016

[Jaini2018a]
Jaini, Priyank and Ghose Amur and Poupart, Pascal
Prometheus: Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks
PGM 2018

[Joshi2018]
Joshi, Himanshu, Paul S. Rosenbloom, and Volkan Ustun
Exact, Tractable Inference in the Sigma Cognitive Architecture via Sum-Product Networks
Advances in Cognitive Systems 6 (2018)

[Krakovna2016]
Krakovna, Viktoriya and Looks, Moshe
A Minimalistic Approach to Sum-Product Network Learning for Real Applications
ICLR 2016

[Lee2013]
Lee, Sang-Woo and Heo, Min-Oh and Zhang, Byoung-Tak
Online Incremental Structure Learning of Sum-Product Networks
ICONIP 2013

[Lee2014]
Lee, Sang-Woo and Watkins, Christopher and Zhang, Byoung-Tak
Non-Parametric Bayesian Sum-Product Networks
Workshop on Learning Tractable Probabilistic Models 2014

[Li2015]
Weizhuo Li
Combining sum-product network and noisy-or model for ontology matching
Proceedings of the 10th International Workshop on Ontology Matching

[Livni2013]
Livni, Roi and Shalev-Shwartz, Shai and Shamir, Ohad
A Provably Efficient Algorithm for Training Deep Networks
arXiv 2013

[Lowd2013]
Lowd, Daniel and Rooshenas, Amirmohammad
Learning Markov Networks With Arithmetic Circuits
Proceedings of the 16th International Conference on Artificial Intelligence and Statistics 2013

[Martens2014]
Martens, James and Medabalimi, Venkatesh
On the Expressive Efficiency of Sum Product Networks
arXiv/1411.7717

[Mauà2017]
Mauá, Deratani Denis and Cozman Fabio Gagliardi and Conaty, Diarmaid and de Campos, Cassio P.
Credal Sum-Product Networks
ISIPTA 2017

[Mei2018]
Mei, Jun and Jiang, Yong and Tu, Kewei
Maximum A Posteriori Inference in Sum-Product Networks
AAAI 2018

[Melibari2016a]
Melibari, Mazen and Poupart, Pascal and Doshi, Prashant
Decision Sum-Product-Max Networks
Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI 2016)

[Melibari2016b]
Melibari, Mazen and Poupart, Pascal and Doshi, Prashant
Sum-Product-Max Networks for Tractable Decision Making
Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems

[Melibari2016c]
Melibari, Mazen and Poupart, Pascal and Doshi, Prashant and Trimponias, George
Dynamic Sum-Product Networks for Tractable Inference on Sequence Data
International Conference on Probabilistic Graphical Models 2016

[Molina2017]
Molina, Alejandro and Natarajan, Sriraam and Kersting, Kristian
Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions
Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017)

[Molina2018]
Molina, Alejandro and Vergari, Antonio and Di Mauro, Nicola and Natarajan, Sriraam and Esposito, Floriana and Kersting, Kristian
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains
Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI 2018)

[Nath2014]
Nath, Aniruddh and Domingos, Pedro
Learning Tractable Statistical Relational Models
Workshop on Learning Tractable Probabilistic Models

[Nath2015]
Nath, Aniruddh and Domingos, Pedro
Learning Relational Sum-Product Networks
Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI 2015)

[Nath2016]
Nath, Aniruddh and Domingos, Pedro
Learning Tractable Probabilistic Models for Fault Localization
Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI 2016)

[Niepert2015]
Niepert, Mathias and Domingos, Pedro
Learning and Inference in Tractable Probabilistic Knowledge Bases
UAI 2015

[Peharz2013]
Peharz, Robert and Geiger, Bernhard and Pernkopf, Franz
Greedy Part-Wise Learning of Sum-Product Networks
ECML-PKDD 2013

[Peharz2014a]
Peharz, Robert and Kapeller, Georg and Mowlaee, Pejman and Pernkopf, Franz
Modeling Speech with Sum-Product Networks: Application to Bandwidth Extension
ICASSP2014

[Peharz2014b]
Robert Peharz and Gens, Robert and Domingos, Pedro
Learning Selective Sum-Product Networks
Workshop on Learning Tractable Probabilistic Models 2014

[Peharz2015a]
Robert Peharz and Tschiatschek, Sebastian and Pernkopf, Franz and Domingos, Pedro
On Theoretical Properties of Sum-Product Networks
Proceedings of the 18th International Conference on Artificial Intelligence and Statistics

[Peharz2015b]
Peharz, Robert
Foundations of Sum-Product Networks for Probabilistic Modeling
PhD Thesis

[Peharz2016]
Robert Peharz and Robert Gens and Franz Pernkopf and Pedro Domingos
On the Latent Variable Interpretation in Sum-Product Networks
arxiv.org/abs/1601.06180

[Peharz2018]
Robert Peharz and Antonio Vergari and Karl Stelzner and Alejandro Molina and Martin Trapp and Kristian Kersting and Zoubin Ghahramani
Probabilistic Deep Learning using Random Sum-Product Networks
arxiv.org/abs/1806.01910

[Poon2011]
Poon, Hoifung and Domingos, Pedro
Sum-Product Network: a New Deep Architecture
UAI 2011

[Pronobis2017a]
Pronobis, A. and Riccio, F. and Rao, R.~P.~N.
Deep Spatial Affordance Hierarchy: Spatial Knowledge Representation for Planning in Large-scale Environments
SSRR 2017

[Pronobis2017b]
Pronobis, A. and Ranganath, A. and Rao, R.~P.~N.
LibSPN: A Library for Learning and Inference with Sum-Product Networks and TensorFlow
Principled Approaches to Deep Learning Workshop at ICML 2017

[Rahman2016]
Tahrima Rahman and Vibhav Gogate
Merging Strategies for Sum-Product Networks: From Trees to Graphs
UAI 2016

[Rashwan2016]
Rashwan, Abdullah and Zhao, Han and Poupart, Pascal
Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics

[Rashwan2018a]
Rashwan, Abdullah and Poupart, Pascal and Zhitang, Chen
Discriminative Training of Sum-Product Networks by Extended Baum-Welch
PGM 2018

[Rashwan2018b]
Rashwan, Abdullah and Kalra, Agastya and Poupart, Pascal and Doshi, Prashant and Trimponias, George and Hsu, Wei-Shou
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
NIPS 2018

[Ratajczak2014]
Ratajczak, Martin and Tschiatschek, S and Pernkopf, F
Sum-Product Networks for Structured Prediction: Context-Specific Deep Conditional Random Fields
Workshop on Learning Tractable Probabilistic Models 2014

[Ratajczak2018]
Ratajczak, Martin and Tschiatschek, S and Pernkopf, F
Sum-Product Networks for Sequence Labeling
preprint

[Rathke2017]
Rathke, F.; Desana, M. and Schnörr, C.
Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans
MICCAI 2017

[Rooshenas2014]
Rooshenas, Amirmohammad and Lowd, Daniel
Learning Sum-Product Networks with Direct and Indirect Variable Interactions
ICML 2014

[Rooshenas2016]
Rooshenas, Amirmohammad and Lowd, Daniel
Discriminative Structure Learning of Arithmetic Circuits
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics

[Sharir2018]
Sharir, Or and Shashua, Amnon
** Sum-Product-Quotient Networks**
AISTATS 2018

[Sguerra2016]
Sguerra, Bruno Massoni and Cozman, Fabio G.
Image Classification Using Sum-Product Networks for Autonomous Flight of Micro Aerial Vehicles
BRACIS 2016 - 5th Brazilian Conference on Intelligent Systems

[Stuhlmueller2012]
Stuhlmuller, Andreas and Goodman, Noah D.
A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs
StaRAI 2012

[Trapp2016]
Trapp, Martin and Peharz, Robert and Skowron, Marcin and Madl, Tamas and Pernkopf, Franz and Trappl, Robert
Structure Inference in Sum-Product Networks using Infinite Sum-Product Trees
Workshop on Practical Bayesian Nonparametrics at NIPS 2016

[Trapp2017]
Trapp, Martin and Madl, Tamas and Peharz, Robert and Pernkopf, Franz and Trappl, Robert
Safe Semi-Supervised Learning of Sum-Product Networks
UAI 2017

[Trapp2018]
Trapp, Martin and Peharz, Robert and Rasmussen, Carl and Pernkopf, Franz
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
Workshop on Tractable Probabilistic Models

[Vergari2015]
Vergari, Antonio and Di Mauro, Nicola and Esposito, Floriana
Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning
ECML-PKDD 2015

[Vergari2017]
Vergari, Antonio and Peharz, Robert and Di Mauro, Nicola and Esposito, Floriana
Encoding and Decoding Representations with Sum- and Max-Product Networks
ICLR 2017 - Workshop Track

[Vergari2018a]
Vergari, Antonio and Peharz, Robert and Di Mauro, Nicola and Molina, Alejandro and Kersting, Kristian and Esposito, Floriana
Sum-Product Autoencoding: Encoding and Decoding Representations with Sum-Product Networks
Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI 2018)

[Vergari2018b]
Vergari, Antonio and Di Mauro, Nicola and Esposito, Floriana
Visualizing and Understanding Sum-Product Networks
Machine Learning Journal

  • [Wang2015]

Wang, Jinghua and Wang, Gang
Hierarchical Spatial Sum-Product Networks for action recognition in Still Images
arXiv:1511.05292

  • [Yuan2016]

Zehuan Yuan and Hao Wang and Limin Wang and Tong Lu and Shivakumara Palaiahnakote and Chew Lim Tan
Modeling Spatial Layout for Scene Image Understanding Via a Novel Multiscale Sum-Product Network
Expert Systems with Applications

[Zhao2015]
Zhao, Han and Melibari, Mazen and Poupart, Pascal
On the Relationship between Sum-Product Networks and Bayesian Networks
ICML 2015

[Zhao2016a]
Zhao, Han and Adel, Tameem and Gordon, Geoff and Amos, Brandon
Collapsed Variational Inference for Sum-Product Networks
ICML 2016

[Zhao2016b]
Zhao, Han and Poupart, Pascal and Gordon, Geoff
A Unified Approach for Learning the Parameters of Sum-Product Networks
NIPS 2016

[Zhao2017]
Zhao, Han and Gordon, Geoff and Poupart, Pascal
Efficient Computation of Moments in Sum-Product Networks
NIPS 2017

[Zheng2018]
_Zheng, Kaiyu and Pronobis, Andrzej and Rao, Rajesh P.N.
Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
AAAI 2018