张 育培

Computer Science

1. Convolutional Neural Network 100%
2. Ultrasound Image 83%
3. Federated Learning 79%
4. Contrastive Learning 66%
5. Representation Learning 56%
6. Image Classification 50%
7. Low-Rank Representation 50%
8. Text Classification 50%
9. Dictionary Learning 50%
10. Long Short-Term Memory Network 45%
11. Dimensionality Reduction 41%
12. Learning Performance 41%
13. Image Representation 38%
14. Experimental Result 37%
15. Classification Performance 35%
16. Side Information 34%
17. Deep Knowledge Tracing 33%
18. Sparsity 33%
19. Deep Learning Model 33%
20. Lstm 33%
21. Educational Data Mining 31%
22. Prediction Accuracy 31%
23. Neural Network 31%
24. Optimization Problem 29%
25. Learning Framework 27%
26. Sparse Representation 27%
27. Contrastive Loss 26%
28. Data Representation 26%
29. Traditional Method 25%
30. Prerequisite Relation 25%
31. Learning Process 22%
32. Computer Science 22%
33. Learning Experiences 20%
34. Data Classification 20%
35. Neighborhood Structure 18%
36. Science Learning 18%
37. Spatiotemporal Graph 16%
38. Mathematical Education 16%
39. ensemble classifier 16%
40. Autoencoder 16%
41. Bayesian Perspective 16%
42. nonrigid registration 16%
43. Local Structure 16%
44. Formal Education 16%
45. Classification Method 16%
46. Process Data 16%
47. Diagnosis Concept 16%
48. Classification Framework 16%
49. Prediction Technique 16%
50. Matrix Factorization 16%