Amortised neural inference offers significant advantages in terms of speed and efficiency compared to traditional Markov Chain Monte Carlo (MCMC) methods. However, there are some drawbacks and limitations to consider:
Loss of Uncertainty Quantification: Amortised neural inference methods may struggle to provide accurate uncertainty estimates compared to MCMC methods, which naturally capture the uncertainty in the posterior distribution. This limitation can be addressed by incorporating techniques like Bayesian neural networks or using ensemble methods to quantify uncertainty.
Overfitting: Neural networks are prone to overfitting, especially in high-dimensional spaces, which can lead to poor generalization and inaccurate inference results. Regularization techniques and careful hyperparameter tuning can help mitigate overfitting.
Complexity and Interpretability: Neural networks are often considered black-box models, making it challenging to interpret the reasoning behind the inference results. Techniques like sensitivity analysis or model distillation can enhance interpretability.
Data Efficiency: Amortised neural inference methods may require large amounts of data for training to achieve optimal performance. Data augmentation, transfer learning, or semi-supervised learning approaches can address data efficiency issues.
Computational Resources: Training complex neural networks for amortised inference may require significant computational resources. Utilizing parallel computing, distributed training, or optimizing network architectures can help manage computational costs.
By addressing these limitations through appropriate techniques and methodologies, the drawbacks of amortised neural inference can be mitigated, leading to more reliable and accurate inference results.