Need help with awesome-spn?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

arranger1044
236 Stars 45 Forks 135 Commits 11 Opened issues

Description

A structured list of resources about Sum-Product Networks (SPNs)

Services available

!
?

Need anything else?

Contributors list

# 487,650
R
C++
Shell
graphic...
85 commits
# 430,038
Shell
R
C++
graphic...
7 commits
# 166,713
julia-l...
juliala...
markov-...
bayesia...
5 commits
# 681,905
3 commits
# 61,924
arxiv
R
Shell
julia-l...
3 commits
# 518,398
3 commits
# 575,841
analysi...
TeX
Shell
CSS
2 commits
# 539,036
1 commit
# 108,259
Python
Jupyter...
pytorch
jax
1 commit
# 630,048
1 commit

Discontinued

awesome-spn has been discontinued as of 01/01/2021!

Please visit and contribute to the website and repo on probabilistic circuits

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

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

Topics

Survey

Weight Learning

Structure Learning

Representation Learning

Modeling

Applications

Theory

Hardware

Related Works

Arithmetic Circuits

Other TPMs

Exploiting Sum-Product Theorem

Resources

Dataset

Code

Talks and Tutorials

Blog Posts

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

  • [Butz2019]
    Butz, Cory J and Oliveira, Jhonatan S. and dos Santos, André E. and Teixeira, André L.
    Deep Convolutional Sum-Product Networks
    AAAI 2019

  • [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

    [Jaini2018b]
    Jaini, Priyank and Poupart, Pascal and Yu, Yaoliang
    Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
    NIPS 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)

  • [Ko2018]
    Ko, Ching-Yun and Chen, Cong and Zhang, Yuke and Batselier, Kim and Wong, Ngai
    Deep Compression of Sum-Product Networks on Tensor Networks
    arXiv 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)

  • [Molina2019]
    Molina, Alejandro and Vergari, Antonio and Stelzner, Karl and Peharz, Robert and Subramani, Pranav and Di Mauro, Nicola and Poupart, Pascal and Kersting, Kristian
    SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
    arXiv:1901.03704

  • [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

  • [Paris2020]
    París, Iago and Sánchez-Cauce, Raquel and Díez, Francisco Javier
    Sum-product networks: A survey
    arXiv:2004.01167

  • [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

  • [Peharz2019]
    Robert Peharz and Antonio Vergari and Karl Stelzner and Alejandro Molina and Martin Trapp and Xiaoting Shao and Kristian Kersting and Zoubin Ghahramani
    Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning
    UAI 2019

  • [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

  • [Shao2019]
    Shao, Xiaoting and Molina, Alejandro and Vergari, Antonio and Stelzner, Karl and Peharz, Robert and Liebig, Thomas and Kersting, Kristian
    Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures
    arXiv:1905.08550

  • [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

  • [Stelzner2019]
    Stelzner, Karl and Peharz, Robert and Kersting, Kristian
    Faster Attend-Infer-Repeat with Tractable Probabilistic Models
    ICML 2019

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

  • [Sommer2018]
    Sommer, Lukas and Oppermann, Julian and Molina, Alejandro and Binnig, Carsten and Kersting, Kristian and Koch, Andreas
    Automatic Mapping of the Sum-Product Network Inference Problem to FPGA-Based Accelerators
    ICCD 2018

  • [Tan2019]
    Tan, Ping Liang, and Peharz, Robert
    Hierarchical Decompositional Mixtures of Variational Autoencoders
    ICML 2019

  • [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

  • [Trapp2019]
    Trapp, Martin and Peharz, Robert and Ge, Hong and Pernkopf, Franz and Ghahramani, Zoubin
    Bayesian Learning of Sum-Product Networks
    NeurIPS 2019

  • [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

  • [Vergari2019]
    Vergari, Antonio and Molina, Alejandro and Peharz, Robert and Ghahramani, Zoubin and Kersting, Kristian and Valera, Isabel
    Automatic Bayesian Density Analysis
    Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019)

  • [Wang2015]
    Wang, Jinghua and Wang, Gang
    Hierarchical Spatial Sum-Product Networks for action recognition in Still Images
    arXiv:1511.05292

  • [Wolfshaar2019]
    van de Wolfshaar, Jos and Pronobix, Andrzej
    Deep Convolutional Sum-Product Networks for Probabilistic Image Representations
    arXiv:1902.06155

  • [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

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.