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.
awesome-spn is released under Public Domain. Feel free to complete and/or correct any of these lists. Pull requests are very welcome!
- [DiMauro2017]
Alternative Variable Splitting Methods to Learn Sum-Product Networks
structure-learning
- [Desana2017]
Sum-Product Graphical Models
arXiv
modeling
- [Pronobis2017b] LibSPN: A Library for Learning and Inference with Sum-Product Networks and TensorFlow PADL@ICML 2017
code
- [Friesen2017] Unifying Sum-Product Networks and Submodular Fields PADL@ICML 2017
applications
modeling
- [Pronobis2017a] Deep Spatial Affordance Hierarchy: Spatial Knowledge Representation for Planning in Large-scale Environments SSRR 2017
applications
- [Mei2017] Maximum A Posteriori Inference in Sum-Product Networks arXiv
theory
- [Rathke2017] Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans MICCAI 2017
applications
- [Trapp2017] Safe Semi-Supervised Learning of Sum-Product Networks UAI 2017
weight learning
- [Mauà2017] Credal Sum-Product Networks ISIPTA 2017
modeling
- [Conaty2017] Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks UAI 2017
theory
- [Zhao2017] Efficient Computation of Moments in Sum-Product Networks arXiv
weight-learning
- [Vergari2017] Encoding and Decoding Representations with Sum- and Max-Product Networks ICLR 2017 - Workshop
representation learning
- [Hsu2017] Online Structure Learning for Sum-Product Networks with Gaussian Leaves ICLR 2017 - Workshop
structure-learning
- [Gens2017] Compositional Kernel Machines ICLR 2017 - Workshop
modeling
- [Molina2017] Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions AAAI2017
modeling
- [Sguerra2016] Image Classification Using Sum-Product Networks for Autonomous Flight of Micro Aerial Vehicles BRACIS 2016
applications
- [Trapp2016] Structure Inference in Sum-Product Networks using Infinite Sum-Product Trees Practical Bayesian Nonparametrics
structure-learning
- [Vergari2016] Visualizing and Understanding Sum-Product Networks arXiv
representation learning
- [Melibari2016c] Dynamic Sum-Product Networks for Tractable Inference on Sequence Data
PGM2016
modeling
structure-learning
- [Jaini2016]
Online Algorithms for Sum-Product Networks with Continuous Variables
PGM2016
weight-learning
- [Desana2016]
Learning Arbitrary Sum-Product Network Leaves with Expectation-Maximization
arXiv
weight-learning
- [Peharz2016]
On the Latent Variable Interpretation in Sum-Product Networks
arXiv
theory
weight-learning
- [Zhao2016b]
A unified approach for learning the parameters of sum-product networks NIPS 2016
weight-learning
- [Yuan2016]
Modeling Spatial Layout for Scene Image Understanding Via a Novel Multiscale Sum-Product Network
Expert Systems and Applications
applications
- [Rahman2016]
Merging Strategies for Sum-Product Networks: From Trees to Graphs
UAI2016
structure-learning
- [Friesen2016]
The Sum-Product Theorem: A Foundation for Learning Tractable Models
ICML2016
theory
- [Zhao2016a]
Collapsed Variational Inference for Sum-Product Networks
ICML2016
weight-learning
- [Rashwan2016]
Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks
AISTATS2016
weight-learning
- [Krakovna2016]
A Minimalistic Approach to Sum-Product Network Learning for Real Applications
ICLR2016
structure-learning
- [Melibari2016b]
Sum-Product-Max Networks for Tractable Decision Making
AAMAS2016
modeling
- [Melibari2016a] Decision Sum-Product-Max Networks
AAAI2016
modeling
structure-learning
- [Nath2016]
Learning Tractable Probabilistic Models for Fault Localization
AAAI2016
applications
- [Peharz2015b]
Foundations of Sum-Product Networks for Probabilistic Modeling
Thesis
theory
- [Wang2015]
Hierarchical Spatial Sum-Product Networks for action recognition in Still Images
arXiv
applications
- [Amer2015]
Sum Product Networks for Activity Recognition
TPAMI2015
applications
- [Li2015]
Combining Sum-Product Network and Noisy-OrModel for Ontology Matching
OM2015
applications
- [Vergari2015]
Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning
ECML-PKDD2015
structure-learning
- [Dennis2015]
Greedy Structure Search for Sum-Product Networks IJCAI2015
structure-learning
- [Friesen2015]
Recursive Decomposition for Nonconvex Optimization
IJCAI2015
theory
- [Niepert2015]
Learning and Inference in Tractable Probabilistic Knowledge Bases
UAI2015
modeling
- [Adel2015]
Learning the Structure of Sum-Product Networks via an SVD-based Algorithm
UAI2015
structure-learning
- [Zhao2015]
On the Relationship between Sum-Product Networks and Bayesian Networks
ICML2015
theory
- [Peharz2015a]
On Theoretical Properties of Sum-Product Networks
AISTATS2015
theory
- [Nath2015]
Learning Relational Sum-Product Networks
AAAI2015
modeling
- [Martens2014]
On the Expressive Efficiency of Sum Product Networks
arXiv
theory
- [Cheng2014]
Language Modeling with Sum-Product Networks
INTERSPEECH2014
modeling
applications
- [Peharz2014a]
Modeling Speech with Sum-Product Networks: Application to Bandwidth Extension
ICASSP2014
applications
- [Lee2014]
Non-Parametric Bayesian Sum-Product Networks
LTPM2014
structure-learning
- [Ratajczak2014]
Sum-Product Networks for Structured Prediction: Context-Specific Deep Conditional Random Fields
LTPM2014
applications
- [Nath2014]
Learning Tractable Statistical Relational Models
LTPM2014
modeling
- [Peharz2014b]
Learning Selective Sum-Product Networks
LTPM2014
weight-learning
modeling
- [Rooshenas2014]
Learning Sum-Product Networks with Direct and Indirect Interactions
ICML2014
structure-learning
- [Lee2013]
Online Incremental Structure Learning of Sum-Product Networks
ICONIP2013
structure-learning
- [Peharz2013]
Greedy Part-Wise Learning of Sum-Product Networks
ECML-PKDD2013
structure-learning
- [Gens2013]
Learning the Structure of Sum-Product Networks
ICML2013
structure-learning
- [Gens2012]
Discriminative Learning of Sum-Product Networks
NIPS2012
weight-learning
- [Dennis2012]
Learning the Architecture of Sum-Product Networks Using Clustering on Variables
NIPS2012
structure-learning
- [Stuhlmueller2012]
Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs
StaRAI2012
modeling
- [Amer2012]
Sum-product Networks for Modeling Activities with Stochastic Structure
CVPR2012
applications
- [Delalleau2011]
Shallow vs. Deep Sum-Product Networks
NIPS2011
theory
- [Poon2011]
Sum-Product Networks: A New Deep Architecture
UAI2011
modeling
weight-learning
- [Trapp2017]
Safe Semi-Supervised Learning of Sum-Product Networks
semi supervised
- [Zhao2017]
Efficient Computation of Moments in Sum-Product Networks
ADF
- [Jaini2016]
Online Algorithms for Sum-Product Networks with Continuous Variables
OBMM
- [Desana2016]
Learning Arbitrary Sum-Product Network Leaves with Expectation-Maximization
EM
- [Zhao2016b]
A unified approach for learning the parameters of sum-product networks
CCCP
- [Zhao2016a]
Collapsed Variational Inference for Sum-Product Networks
variational method
- [Rashwan2016]
Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks
OBMM
EGD
- [Peharz2014b]
Learning Selective Sum-Product Networks
ML
SSPN
- [Poon2011]
Sum-Product Networks: A New Deep Architecture
EM
Hard EM
SGD
- [Gens2012]
Discriminative Learning of Sum-Product Networks
disc Hard EM
disc Hard SGD
- [DiMauro2017]
Alternative Variable Splitting Methods to Learn Sum-Product Networks
RGVS
EBVS
- [Hsu2017] Online Structure Learning for Sum-Product Networks with Gaussian Leaves ICLR 2017 - Workshop
online structure learning
- [Trapp2016] Structure Inference in Sum-Product Networks using Infinite Sum-Product Trees
infiniteSPT
Bayesian nonparametrics
- [Melibari2016c]
Dynamic Sum-Product Networks for Tractable Inference on Sequence Data
hill-climbing
- [Rahman2016]
Merging Strategies for Sum-Product Networks: From Trees to Graphs
pruning
dagSPN
- [Vergari2015]
Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning
LearnSPN-b
LearnSPN-bt
LearnSPN-btb
- [Dennis2015]
Greedy Structure Search for Sum-Product Networks
dagSPN
- [Adel2015]
Learning the Structure of Sum-Product Networks via an SVD-based Algorithm
SPN-SVD
DSPN-SVD
- [Nath2015]
Learning Relational Sum-Product Networks
relational
- [Lee2014]
Non-Parametric Bayesian Sum-Product Networks
non-parametrics
- [Peharz2014b] Learning Selective Sum-Product Networks
SSPN
- [Rooshenas2014] Learning Sum-Product Networks with Direct and Indirect Interactions
ID-SPN
- [Lee2013] Online Incremental Structure Learning of Sum-Product Networks
- [Peharz2013]
Greedy Part-Wise Learning of Sum-Product Networks
bottom-up
- [Gens2013]
Learning the Structure of Sum-Product Networks
top-down
LearnSPN
- [Dennis2012]
Learning the Architecture of Sum-Product Networks Using Clustering on Variables
top-down``k-means
- [Vergari2017] Encoding and Decoding Representations with Sum- and Max-Product Networks
decoding
- [Vergari2016] Visualizing and Understanding Sum-Product Networks
embeddings
- [Desana2017]
Sum-Product Graphical Models
SPGM
- [Mauà2017] Credal Sum-Product Networks
CSPN
- [Gens2017] Compositional Kernel Machines
CKM
- [Friesen2017] Unifying Sum-Product Networks and Submodular Fields
SSPN
- [Molina2017] Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions
Poisson SPNs
- [Melibari2016c] Dynamic Sum-Product Networks for Tractable Inference on Sequence Data
dynamic-SPN
- [Melibari2016b]
Sum-Product-Max Networks for Tractable Decision Making
decision-diagram
- [Melibari2016a]
Decision Sum-Product-Max Networks
decision-diagram
- [Friesen2015]
Recursive Decomposition for Nonconvex Optimization
opt
- [Niepert2015]
Learning and Inference in Tractable Probabilistic Knowledge Bases
relational
- [Nath2015]
Learning Relational Sum-Product Networks
relational
- [Nath2014]
Learning Tractable Statistical Relational Models
relational
- [Peharz2014b] Learning Selective Sum-Product Networks
SSPN
- [Stuhlmueller2012]
Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs
FSPN
- [Poon2011]
Sum-Product Networks: A New Deep Architecture
SPN
- [Pronobis2017a] Deep Spatial Affordance Hierarchy: Spatial Knowledge Representation for Planning in Large-scale Environments SSRR 2017
robot control
- [Rathke2017] Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans MICCAI 2017
segmentation
- [Friesen2017] Submodular Sum-Product Networks for Scene Understanding OpenReview@ICLR 2017
segmentation
- [Sguerra2016]
Image Classification Using Sum-Product Networks for Autonomous Flight of Micro Aerial Vehicles
image-classification
ID-Spn
- [Yuan2016]
Modeling Spatial Layout for Scene Image Understanding Via a Novel Multiscale Sum-Product Network
cv
segmentation
- [Nath2016] Learning Tractable Probabilistic Models for Fault Localization
- [Wang2015]
Hierarchical Spatial Sum-Product Networks for action recognition in Still Images
cv
activity-recognition
- [Amer2015]
Sum Product Networks for Activity Recognition
cv
activity-recognition
- [Li2015] Combining Sum-Product Network and Noisy-OrModel for Ontology Matching
sem-web
- [Cheng2014]
Language Modeling with Sum-Product Networks
sequence
- [Ratajczak2014]
Sum-Product Networks for Structured Prediction: Context-Specific Deep Conditional Random Fields
speech
- [Peharz2014a]
Modeling Speech with Sum-Product Networks: Application to Bandwidth Extension
speech
- [Amer2012]
Sum-product Networks for Modeling Activities with Stochastic Structure
cv``activity-recognition
- [Mei2017] Maximum A Posteriori Inference in Sum-Product Networks
MAP inference
- [Conaty2017] Approximation Complexity of Maximum A Posteriori Inference in Sum-Product Networks
MAP inference
- [Zhao2016b]
A Unified Approach for Learning the Parameters of Sum-Product Networks
CCCP
- [Peharz2016]
On the Latent Variable Interpretation in Sum-Product Networks
EM
- [Friesen2016]
The Sum-Product Theorem: A Foundation for Learning Tractable Models
opt
sum-prod-theorem
- [Peharz2015b] Foundations of Sum-Product Networks for Probabilistic Modeling
- [Friesen2015]
Recursive Decomposition for Nonconvex Optimization
opt
sum-prod-theorem
- [Zhao2015] On the Relationship between Sum-Product Networks and Bayesian Networks
- [Peharz2015a] On Theoretical Properties of Sum-Product Networks
- [Martens2014]
On the Expressive Efficiency of Sum Product Networks
depth
- [Delalleau2011]
Shallow vs. Deep Sum-Product Networks
depth
- [Darwiche2003] [A Differential Approach to Inference in Bayesian Networks](Advances in Neural Information Processing Systems 2011) J. ACM 2003
- [Lowd2013] Learning Markov Networks With Arithmetic Circuits AISTATS 2013
- [Rooshenas2016] Discriminative Structure Learning of Arithmetic Circuits AISTATS 2016
- [Choi2017] On Relaxing Determinism in Arithmetic Circuits ICML 2017
- [Gens2017] Compositional Kernel Machines ICLR 2017 - Workshop
- 20 commonly used datasets for density estimation as in [Lowd2013][Gens2013][Rooshenas2014][Vergari2015][Adel2015][Zhao2016a][Rooshenas2016]
- [Desana2017] SPGM implementation in
C++
- [Pronobis2017b] LibSPN tensorflow implementation with bindings in
python3
- SumProductNetworks.jl Software package for SPNs.
julia
- [Hsu2017] Tachyon structure and parameter learning in
python3
- [Hsu2017] Online structure learning for continuous leaf SPNs
python3
- [Zhao2016a, Zhao2016b] Parameter optimization using MLE and Bayesian approach
spn-opt
C++
- [Vergari2016]
spyn-repr
extracting embeddings from SPNs
python3
- [Vergari2015] spyn LearnSPN-B/T/B and SPN
inference routines in Python
python3
- [Rooshenas2014] ID-SPN and inference routines
on ACs implemented in the
Libra Toolkit
Ocaml
- [Peharz2014a]
ABE-SPN
Artificial Bandwidth-Extension with Sum-Product Networks
MATLAB
C++
- GoSPN implementing
LearnSPN in Go
Go
- [Cheng2014]
lmspn Language modeling
with SPNs
C++
CUDA
- C++/Cuda porting
of Poon's architecture
C++
CUDA
- Python porting
of Poon's architecture
python2
- [Gens2013]
LearnSPN
Java
- [Poon2011] Code to train Poon's architecture
weigths by EM
Java
MPI
- Di Mauro and Vergari Learning Sum-Product Networks tutorial at PGM'16 2016
- Poupart P. Deep Learning, Sum-Product Networks Part I Part II 2015
- Hernàndez-Lobato, J. M. An Introduction to Sum-Product Networks 2013
- Gens, R. Learning the Structure of Sum-Product Networks [Gens2013] 2013
- Poon, H. Sum-Product Networks: A New Deep Architecture [Poon2011] 2011
[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
[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
[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
[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
[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
[Mei2017]
_Mei, Jun and Jiang, Yong and Tu, Kewei_
**Maximum A Posteriori Inference in Sum-Product Networks**
arXiv 2017
[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)
[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
[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
arXiv 2016 *
[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
[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
[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
[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
[Vergari2015]
_Vergari, Antonio and Di Mauro, Nicola and Esposito, Floriana_
**Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning**
ECML-PKDD 2015
[Vergari2016]
_Vergari, Antonio and Di Mauro, Nicola and Esposito, Floriana_
**Visualizing and Understanding Sum-Product Networks**
arXiv:1608.08266
[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
[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**
arXiv