Propose an architectural change to your deep generative model to improve image quality and meet the 10ms latency constraint. Explain the tradeoffs involved in your design choice.
Replace the direct application of the deep generative model with a conditional modeling approach using a Boltzmann machine. This would allow you to model the relationships between different entries in the output image, such as spatial coherence and texture consistency. However, this may require additional computational resources and training data to achieve optimal performance.
Propose an approximation technique to efficiently compute the maximum likelihood gradient for your Boltzmann machine-based recommendation system. Explain the tradeoffs involved in this approach.
Use Markov Chain Monte Carlo (MCMC) methods, such as Gibbs sampling or Langevin dynamics, to approximate the partition function and efficiently compute the maximum likelihood gradient. This involves making assumptions about the posterior distribution and iteratively sampling from it to estimate the required statistics. Tradeoffs include increased computation time per iteration versus reduced memory usage […]