Propose an alternative approach for making predictions in your autonomous vehicle system that balances accuracy with computational efficiency. Explain the tradeoff between this new approach and the principled Bayesian method.
Use a point estimate, such as the Maximum A Posteriori (MAP) estimate, which provides a single value for each parameter while sacrificing some information about the uncertainty.
Propose an optimization strategy for this specific problem that takes into account the non-convex nature of the error surface. Explain the tradeoff.
Use stochastic gradient descent or momentum-based variants to adapt to the complex error surface. This allows the algorithm to converge faster and more reliably, but may require careful tuning of hyperparameters and learning rates to avoid overfitting.
Explain how you would redesign the neural network architecture to mitigate the vulnerability to adversarial attacks. What tradeoffs do you consider when choosing a design?
Replace linear activation functions with non-linear ones (e.g., ReLU, tanh) to reduce the impact of excessive linearity in the learned functions. Additionally, use techniques such as dropout and batch normalization to improve model robustness against adversarial examples.