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DeepStochLog: Neural Stochastic Logic Programming

arXiv 2106.12574 ↗First posted 23 Jun 2021cs.AI cs.LOcited by 58

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Abstract (arXiv)

Recent advances in neural symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural symbolic framework based on stochastic definite clause grammars, a type of stochastic logic program, which defines a probability distribution over possible derivations. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to create a framework that can be trained end-to-end. We show that inference and learning in neural stochastic logic programming scale much better than for neural probabilistic logic programs. Furthermore, the experimental evaluation shows that DeepStochLog achieves state-of-the-art results on challenging neural symbolic learning tasks.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). Builds on 14 of its 55 references that are in the map. First posted on arXiv 23 Jun 2021.

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14 of 55 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  4. 2019Neural Probabilistic Logic Programming in DeepProbLogcited by 389
  5. 2016Solving General Arithmetic Word Problemscited by 353
  6. 2017End-to-End Differentiable Provingcited by 159
  7. 2016Recurrent Neural Network Grammarscited by 109
  8. 2017Logic Tensor Networks for Semantic Image Interpretationcited by 92
  9. 2020From Statistical Relational to Neuro-Symbolic Artificial Intelligencecited by 63
  10. 2020Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoningcited by 58
  11. 2020Neural-Symbolic Integration: A Compositional Perspectivecited by 45
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2 more
  1. 2019Synthesizing Datalog Programs Using Numerical Relaxationcited by 23
  2. 2020Analyzing Differentiable Fuzzy Implicationscited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 6; 2023: 13; 2024: 16; 2025: 11; 2026: 9.

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  1. 2021From Statistical Relational to Neurosymbolic Artificial Intelligence: a Surveycited by 72
  2. 2023Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcutscited by 63
  3. 2023Interpretable Neural-Symbolic Concept Reasoningcited by 56
  4. 2022A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inferencecited by 50
  5. 2022NeuPSL: Neural Probabilistic Soft Logiccited by 34
  6. 2023Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsalcited by 33
  7. 2022VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programmingcited by 31
  8. 2023Neural Probabilistic Logic Programming in Discrete-Continuous Domainscited by 24
  9. 2023Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learningcited by 22
  10. 2022Deep Symbolic Learning: Discovering Symbols and Rules from Perceptionscited by 20
  11. 2024On the Independence Assumption in Neurosymbolic Learningcited by 18
  12. 2024Interpretable Concept-Based Memory Reasoningcited by 17
38 more
  1. 2024Softened Symbol Grounding for Neuro-symbolic Systemscited by 16
  2. 2024KLay: Accelerating Arithmetic Circuits for Neurosymbolic AIcited by 16
  3. 2023Relational Concept Bottleneck Modelscited by 15
  4. 2023Scalable Neural-Probabilistic Answer Set Programmingcited by 14
  5. 2023LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervisioncited by 13
  6. 2023GLUECons: A Generic Benchmark for Learning Under Constraintscited by 9
  7. 2024ULLER: A Unified Language for Learning and Reasoningcited by 9
  8. 2024NeSyA: Neurosymbolic Automatacited by 9
  9. 2024LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulationcited by 8
  10. 2025Defining neurosymbolic AIcited by 7
  11. 2024EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logiccited by 6
  12. 2024Compositional Generalization Across Distributional Shifts with Sparse Tree Operationscited by 5
  13. 2023logLTN: Differentiable Fuzzy Logic in the Logarithm Spacecited by 4
  14. 2024Variable Assignment Invariant Neural Networks for Learning Logic Programscited by 4
  15. 2024Differentiable Logic Programming for Distant Supervisioncited by 4
  16. 2021DeepPSL: End-to-end perception and reasoningcited by 3
  17. 2023Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detectioncited by 3
  18. 2024On the use of neurosymbolic AI for defending against cyber attackscited by 3
  19. 2025Valid Text-to-SQL Generation with Unification-based DeepStochLogcited by 3
  20. 2025Grounding Methods for Neural-Symbolic AIcited by 3
  21. 2024A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AIcited by 2
  22. 2025T-ILR: a Neurosymbolic Integration for LTLfcited by 2
  23. 2026To Neuro-Symbolic Classification and Beyond by Compiling Description Logic Ontologies to Probabilistic Circuitscited by 2
  24. 2023From Probabilistic Programming to Complexity-based Programmingcited by 1
  25. 2024Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learningcited by 1
  26. 2024Declarative Design of Neural Predicates in Neuro-Symbolic Systemscited by 1
  27. 2025A Scalable Approach to Probabilistic Neuro-Symbolic Robustness Verificationcited by 1
  28. 2025Verification Learning: Make Unsupervised Neuro-Symbolic System Feasiblecited by 1
  29. 2025CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learningcited by 1
  30. 2025Curriculum Abductive Learningcited by 1
  31. 2025Understanding and Improving Adversarial Robustness of Neural Probabilistic Circuitscited by 1
  32. 2021A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networkscited by 0
  33. 2022An Algebraic Approach to Learning and Groundingcited by 0
  34. 2022A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoodscited by 0
  35. 2023A Scalable Technique for Weak-Supervised Learning with Domain Constraintscited by 0
  36. 2024Neural logic programs and neural netscited by 0
  37. 2025Neuro-symbolic Weak Supervision: Theory and Semanticscited by 0
  38. 2025Logic of Hypotheses: from Zero to Full Knowledge in Neurosymbolic Integrationcited by 0

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