scholarly journals Handling epistemic and aleatory uncertainties in probabilistic circuits

2022 ◽  
Author(s):  
Federico Cerutti ◽  
Lance M. Kaplan ◽  
Angelika Kimmig ◽  
Murat Şensoy
Keyword(s):  
Probabilistic Circuits

scholarly journals Obfuscation of Probabilistic Circuits and Applications

2015 ◽  
pp. 468-497 ◽  
Author(s):  
Ran Canetti ◽  
Huijia Lin ◽  
Stefano Tessaro ◽  
Vinod Vaikuntanathan
Keyword(s):  
Probabilistic Circuits

High Efficiency and Low Overkill Testing for Probabilistic Circuits

2020 ◽  
Author(s):  
Ming-Ting Lee ◽  
Chen-Hung Wu ◽  
Shi-Tang Liu ◽  
Cheng-Yun Hsieh ◽  
James Chien-Mo Li
Keyword(s):  
High Efficiency ◽  
Probabilistic Circuits

Tomographic Testing and Validation of Probabilistic Circuits

2011 ◽  
Author(s):  
Alexandru Paler ◽  
Armin Alaghi ◽  
Ilia Polian ◽  
John P. Hayes
Keyword(s):  
Probabilistic Circuits

scholarly journals Learning Probabilistic Sentential Decision Diagrams by Sampling

2020 ◽  
Author(s):  
Renato Geh ◽  
Denis Mauá ◽  
Alessandro Antonucci
Keyword(s):  
Neural Network ◽  
Domain Knowledge ◽  
Probabilistic Models ◽  
State Of The Art ◽  
Decision Diagrams ◽  
Structural Constraints ◽  
Competitive Performance ◽  
Probabilistic Circuits ◽  
Probabilistic Queries ◽  
Logical Constraints

Probabilistic circuits are deep probabilistic models with neural-network-like semantics capable of accurately and efficiently answering probabilistic queries without sacrificing expressiveness. Probabilistic Sentential Decision Diagrams (PSDDs) are a subclass of probabilistic circuits able to embed logical constraints to the circuit’s structure. In doing so, they obtain extra expressiveness with empirical optimal performance. Despite achieving competitive performance compared to other state-of-the-art competitors, there have been very few attempts at learning PSDDs from a combination of both data and knowledge in the form of logical formulae. Our work investigates sampling random PSDDs consistent with domain knowledge and evaluating against state-of-the-art probabilistic models. We propose a method of sampling that retains important structural constraints on the circuit’s graph that guarantee query tractability. Finally, we show that these samples are able to achieve competitive performance even on larger domains.

scholarly journals Conditional Sum-Product Networks: Modular Probabilistic Circuits via Gate Functions

2021 ◽  
Author(s):  
Xiaoting Shao ◽  
Alejandro Molina ◽  
Antonio Vergari ◽  
Karl Stelzner ◽  
Robert Peharz ◽  
...  
Keyword(s):  
Probabilistic Circuits
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