Lukas Paul Achatius Galke

Computer Science

1. Graph Neural Network 100%
2. Text Classification 90%
3. Information Retrieval 75%
4. Language Modeling 70%
5. Annotation 64%
6. Neural Network 64%
7. Word Embedding 64%
8. Large Language Model 64%
9. Training Data 63%
10. Autoencoder 51%
11. Data Enrichment 51%
12. Node Classification 51%
13. Lifelong Learning 51%
14. retrieval model 45%
15. Graph Convolution 34%
16. Sparsity 34%
17. Matrix Multiplication 34%
18. Representation Learning 32%
19. Language Development 32%
20. Multilayer Perceptron 31%
21. Digital Library 30%
22. Conceptual Model 25%
23. Logistic Regression 25%
24. Traditional Method 25%
25. Semantic Web 25%
26. Individual Characteristic 25%
27. Class Distribution 25%
28. Inverse Document Frequency 25%
29. Support Vector Machine 25%
30. Semantic Annotation 25%
31. Research Community 25%
32. Language Understanding 25%
33. Programming Language 25%
34. Background Information 25%
35. Deep Learning Method 25%
36. Generalization Ability 25%
37. Embedding Matrix 25%
38. Case Study 25%
39. Scientific Knowledge 25%
40. Deep Neural Network 25%
41. Citation Data 25%
42. Scientific Field 25%
43. Detection Method 25%
44. Scholarly Article 25%
45. Knowledge Graph 25%
46. Performance Comparison 25%
47. Regularization 22%
48. DistilBERT 21%
49. Human Language 19%
50. Neural Network Model 17%