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1. Extracting M of N Rules from Restricted Boltzmann Machines

2. Generalising the Discriminative Restricted Boltzmann Machines

3. Learning and Reasoning with Logic Tensor Networks

11. Background

12. Introduction and Overview

13. Representing, Learning and Extracting Temporal Knowledge from Neural Networks: A Case Study

14. Argumentation Neural Networks

15. Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language

16. Experiments with LLMs for Converting Language to Logic

17. Bringing Back Semantics to Knowledge Graph Embeddings: An Interpretability Approach

18. LENs for Analyzing the Quality of Life of People with Intellectual Disability

19. ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models

20. Enhancing GPT-Based Planning Policies by Model-Based Plan Validation

21. Error-Margin Analysis for Hidden Neuron Activation Labels

22. Can Large Language Models Put 2 and 2 Together? Probing for Entailed Arithmetical Relationships

23. Reasoning in Transformers – Mitigating Spurious Correlations and Reasoning Shortcuts

24. Assessing LLMs Suitability for Knowledge Graph Completion

25. ProSLM: A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering

26. TIC: Translate-Infer-Compile for Accurate 'Text to Plan' Using LLMs and Logical Representations

27. ECATS: Explainable-by-Design Concept-Based Anomaly Detection for Time Series

28. Concept Induction Using LLMs: A User Experiment for Assessment

29. Enhancing Logical Tensor Networks: Integrating Uninorm-Based Fuzzy Operators for Complex Reasoning

30. Logic Supervised Learning for Time Series - Continual Learning for Appliance Detection

31. On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis

32. Parameter Learning Using Approximate Model Counting

33. WineGraph: A Graph Representation for Food-Wine Pairing

34. Bayesian Inverse Graphics for Few-Shot Concept Learning

35. IID Relaxation by Logical Expressivity: A Research Agenda for Fitting Logics to Neurosymbolic Requirements

36. Variable Assignment Invariant Neural Networks for Learning Logic Programs

37. Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction

38. Towards Understanding the Impact of Graph Structure on Knowledge Graph Embeddings

39. Towards Understanding Graph Neural Networks: Functional-Semantic Activation Mapping

40. Enhancing Neuro-Symbolic Integration with Focal Loss: A Study on Logic Tensor Networks

41. Metacognitive AI: Framework and the Case for a Neurosymbolic Approach

42. Commonsense Ontology Micropatterns

43. Lattice-Preserving Ontology Embeddings

44. Towards Learning Abductive Reasoning Using VSA Distributed Representations

45. On the Use of Neurosymbolic AI for Defending Against Cyber Attacks

46. Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation

47. Simple and Effective Transfer Learning for Neuro-Symbolic Integration

48. ULLER: A Unified Language for Learning and Reasoning

49. A Fuzzy Loss for Ontology Classification

50. Assessing Logical Reasoning Capabilities of Encoder-Only Transformer Models

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