About Current Machine Learning
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The journal Current Machine Learning serves as an advanced forum for innovative studies and major trends of theoretical, methodological, and practical aspects of machine learning. Our aim is to provide a comprehensive and reliable source of information on the current advances and future perspectives from diverse disciplines that intersect with machine learning.
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Areas of interest include:
1. Active Learning
2. Adversarial Machine Learning
3. Anomaly Detection
4. Applications in Finance
5. Applications in Healthcare
6. Applications in Robotics
7. AutoML (Automated Machine Learning)
8. Bayesian Methods in Machine Learning
9. Computational Learning Theory
10. Computer Vision
11. Data Preprocessing and Augmentation
12. Deep Learning
13. Dimensionality Reduction
14. Ensemble Methods
15. Ethics and Fairness in AI
16. Evolutionary Algorithms
17. Feature Selection and Extraction
18. Federated Learning
19. Game Theory for Machine Learning
20. Generative Adversarial Networks
21. Generative AI
22. Graph-based Learning
23. Interpretability and Explainability
24. LARGE LANGUAGE MODELS
25. META-LEARNING
26. Model Evaluation and Validation
27. Multi-task Learning
28. Natural Language Processing
29. Neural Architecture Search
30. Object Detection
31. Online Learning
32. Optimization Techniques
33. Quantum Machine Learning
34. Recommender Systems
35. Reinforcement Learning
36. Retrieval-Augmented Generation
37. Robustness and Adversarial Machine Learning
38. Semi-parametric and Non-parametric Methods
39. Semi-supervised Learning
40. Speech Recognition
41. Statistical Learning Theory
42. Supervised Learning
43. Time Series Analysis
44. Transfer Learning
45. Unsupervised Learning