225 results on '"d'Avila Garcez, Artur"'
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2. Logic Tensor Networks
3. A Practical tutorial on Explainable AI Techniques
4. Chapter 17. Logic Tensor Networks: Theory and Applications
5. Extracting M of N Rules from Restricted Boltzmann Machines
6. Generalising the Discriminative Restricted Boltzmann Machines
7. Learning and Reasoning with Logic Tensor Networks
8. Speaker recognition with hybrid features from a deep belief network
9. Fat-Fast VG-RAM WNN: A high performance approach
10. Neural-Symbolic Integration: The Road Ahead
11. Experiments on Theory Refinement
12. Knowledge Extraction from Trained Networks
13. Experiments on Handling Inconsistencies
14. Experiments on Knowledge Extraction
15. Handling Inconsistencies in Neural Networks
16. Theory Refinement in Neural Networks
17. Background
18. Introduction and Overview
19. Representing, Learning and Extracting Temporal Knowledge from Neural Networks: A Case Study
20. A neural cognitive model of argumentation with application to legal inference and decision making
21. Generalising the Discriminative Restricted Boltzmann Machines
22. Extracting M of N Rules from Restricted Boltzmann Machines
23. Argumentation Neural Networks
24. Learning and Reasoning with Logic Tensor Networks
25. Accountability in AI: From principles to industry-specific accreditation
26. A Practical Guide on Explainable Ai Techniques Applied on Biomedical Use Case Applications
27. Fast relational learning using bottom clause propositionalization with artificial neural networks
28. Connectionist modal logic: Representing modalities in neural networks
29. Representing, Learning and Extracting Temporal Knowledge from Neural Networks: A Case Study
30. Connectionist computations of intuitionistic reasoning
31. The Connectionist Inductive Learning and Logic Programming System
32. Geometric semi-automatic analysis of radiographs of Colles’ fractures
33. Neuro-Symbolic Probabilistic Argumentation Machines
34. A machine learning approach for Colles’ fracture treatment diagnosis
35. Argumentation Neural Networks
36. Experiments on Theory Refinement
37. Introduction and Overview
38. Experiments on Knowledge Extraction
39. Neural-Symbolic Integration: The Road Ahead
40. Handling Inconsistencies in Neural Networks
41. Neural-Symbolic Learning Systems
42. Knowledge Extraction from Trained Networks
43. Background
44. Experiments on Handling Inconsistencies
45. Theory Refinement in Neural Networks
46. Editorial: Booming of Neural Networks and Learning Systems
47. Accountability in AI: From principles to industry-specific accreditation.
48. Human-Like Neural-Symbolic Computing (Dagstuhl Seminar 17192)
49. Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks
50. Human-Like Neural-Symbolic Computing (Dagstuhl Seminar 17192)
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