Neil Robertson

Computer Science

1. Networks 100%
2. Classes 72%
3. Art Performance 60%
4. Embedding 60%
5. Accuracy 60%
6. Algorithms 58%
7. Detection 50%
8. Deep Learning 45%
9. Functions 39%
10. Automatic Target Recognition 38%
11. Structured Information 32%
12. Discriminative Power 32%
13. Optimization 32%
14. Semantic Similarity 29%
15. Real Time Performance 29%
16. Slow Convergence 29%
17. Detection Algorithm 29%
18. Deep Neural Network 27%
19. Convolutional Neural Network 27%
20. Landmark Detection 26%
21. Benchmark 26%
22. Generalization 24%
23. Design 24%
24. Convolutional Neural Network 22%
25. Kalman Filtering 21%
26. Face Recognition 21%
27. Field Programmable Gate Arrays 21%
28. Classification (Machine Learning) 21%
29. Optimization Problem 21%
30. Instance Level 21%
31. Aggregation Level 21%
32. Video Summarization 21%
33. Speaker Diarization 21%
34. Reinforcement Learning 21%
35. Embedding Space 21%
36. Database 21%
37. Domains 21%
38. Language Modeling 21%
39. Selection Policy 21%
40. Global Information 21%
41. Training Data 21%
42. Data Distribution 21%
43. Deep Feature 21%
44. Recurrent Neural Network 21%
45. Generative Adversarial Networks 21%
46. Likelihood Estimation 21%
47. Image Enhancement 21%
48. Heterogeneous System 18%
49. Lowest Resolution 18%
50. Heterogeneous Platform 18%