mlsanityData to 7 Oct 2026

Neural Probabilistic Logic Programming in DeepProbLog

arXiv 1907.08194 ↗First posted 18 Jul 2019cs.AIcited by 389

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Cited by 79 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.

Key facts. Cited by 389 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jul 2019.

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Citing papers per year: 2021: 46; 2022: 38; 2023: 67; 2024: 67; 2025: 88; 2026: 83.

389 papers.

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  1. 2023LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Proverscited by 161
  2. 2022Semantic Probabilistic Layers for Neuro-Symbolic Learningcited by 103
  3. 2023NeurASP: Embracing Neural Networks into Answer Set Programmingcited by 92
  4. 2021From Statistical Relational to Neurosymbolic Artificial Intelligence: a Surveycited by 72
  5. 2023Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcutscited by 63
  6. 2021DeepStochLog: Neural Stochastic Logic Programmingcited by 58
  7. 2021Incorporating Domain Knowledge into Deep Neural Networkscited by 56
  8. 2023Interpretable Neural-Symbolic Concept Reasoningcited by 56
  9. 2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
  10. 2022A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inferencecited by 50
  11. 2021MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networkscited by 44
  12. 2022Deep Learning with Logical Constraintscited by 44
38 more
  1. 2024R2-Guard: Robust Reasoning Enabled LLM Guardrail via Knowledge-Enhanced Logical Reasoningcited by 41
  2. 2021Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use casescited by 39
  3. 2021A Survey on Neural-symbolic Learning Systemscited by 39
  4. 2022Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Reviewcited by 37
  5. 2021Neuro-Symbolic Inductive Logic Programming with Logical Neural Networkscited by 36
  6. 2024BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcutscited by 36
  7. 2024Learning Iterative Reasoning through Energy Diffusioncited by 36
  8. 2022NeuPSL: Neural Probabilistic Soft Logiccited by 34
  9. 2022SIMPLE: A Gradient Estimator for k-Subset Samplingcited by 34
  10. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
  11. 2023Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsalcited by 33
  12. 2022VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programmingcited by 31
  13. 2021EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use casecited by 30
  14. 2024How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Datacited by 29
  15. 2024HardNet: Hard-Constrained Neural Networks with Universal Approximation Guaranteescited by 29
  16. 2021Learning Algebraic Representation for Systematic Generalization in Abstract Reasoningcited by 27
  17. 2022Neuro-Symbolic Entropy Regularizationcited by 27
  18. 2021Differentiable Inductive Logic Programming for Structured Examplescited by 26
  19. 2021Techniques for Symbol Grounding with SATNetcited by 24
  20. 2021Neuro-Symbolic Hierarchical Rule Inductioncited by 23
  21. 2024Scaling Tractable Probabilistic Circuits: A Systems Perspectivecited by 23
  22. 2022RobustLR: Evaluating Robustness to Logical Perturbation in Deductive Reasoningcited by 22
  23. 2023Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learningcited by 22
  24. 2023Human-like Few-Shot Learning via Bayesian Reasoning over Natural Languagecited by 21
  25. 2023Learning to Solve Constraint Satisfaction Problems with Recurrent Transformercited by 21
  26. 2023A Pseudo-Semantic Loss for Autoregressive Models with Logical Constraintscited by 21
  27. 2024TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphscited by 21
  28. 2025Continuous Thought Machinescited by 21
  29. 2022Deep Symbolic Learning: Discovering Symbols and Rules from Perceptionscited by 20
  30. 2021ProTo: Program-Guided Transformer for Program-Guided Taskscited by 19
  31. 2024A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcutscited by 19
  32. 2021Differentiable Logic Machinescited by 18
  33. 2023Improved Logical Reasoning of Language Models via Differentiable Symbolic Programmingcited by 18
  34. 2024On the Independence Assumption in Neurosymbolic Learningcited by 18
  35. 2025Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraintscited by 18
  36. 2023LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoningcited by 17
  37. 2024End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanationscited by 17
  38. 2024Interpretable Concept-Based Memory Reasoningcited by 17

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