1. How does variational inference work, and how is it used in Bayesian neural networks?
2. What is the Evidence Lower Bound (ELBO), and why is it important in variational autoencoders?
3. How does Monte Carlo dropout approximate Bayesian inference in deep learning?
4. What are the key challenges in training deep reinforcement learning agents?
5. How does the double Q-learning algorithm address the overestimation bias in traditional Q-learning?
6. What is the difference between on-policy and off-policy reinforcement learning algorithms?
7. How do soft actor-critic (SAC) methods improve sample efficiency in reinforcement learning?
8. What is the KL divergence, and how is it used in policy optimization algorithms?
9. How do policy gradient methods work, and what are their limitations?
10. What is the role of entropy regularization in reinforcement learning?
11. How do intrinsic motivation and curiosity-based methods improve exploration in reinforcement learning?
12. What are the challenges of multi-agent reinforcement learning?
13. How does multi-task learning differ from single-task learning in deep neural networks?
14. How is multi-objective optimization handled in machine learning?
15. What is the role of hierarchical reinforcement learning in solving complex tasks?
16. How do neural architecture search (NAS) methods optimize the structure of neural networks?
17. What is the difference between evolutionary algorithms and gradient-based optimization techniques?
18. How do hypernetworks generate weights for neural networks?
19. What is meta-learning, and how does it allow models to learn to learn?
20. How do model-based reinforcement learning algorithms differ from model-free methods?
21. How does gradient clipping help prevent exploding gradients in deep learning?
22. What is the difference between actor-critic and advantage actor-critic (A2C) methods?
23. What is a trust region in policy optimization, and how does it stabilize training?
24. How does trust region policy optimization (TRPO) work, and why is it effective?
25. How does proximal policy optimization (PPO) differ from TRPO?
26. How do attention mechanisms work in sequence models, and what problem do they solve?
27. What is the purpose of multi-head attention in Transformer models?
28. How does self-attention work in Transformer architectures like BERT?
29. How do Transformer models handle long-range dependencies better than RNNs?
30. What is masked language modeling, and how does it benefit pre-trained language models like BERT?
31. How does the GPT architecture differ from BERT in terms of autoregressive modeling?
32. What is the role of positional encoding in Transformer models?
33. What is the significance of transfer learning in NLP using models like GPT-3 and BERT?
34. What are the challenges of fine-tuning pre-trained language models for downstream tasks?
35. How does few-shot and zero-shot learning work in language models like GPT-3?
36. What is the difference between unsupervised, semi-supervised, and self-supervised learning?
37. How does contrastive learning work in self-supervised learning?
38. What are Siamese networks, and how are they used for one-shot learning?
39. How does the triplet loss function work, and how is it used in metric learning?
40. What is a prototypical network, and how does it enable few-shot learning?