No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers

Understand

We consider the sampling problem, where the aim is to draw samples from a distribution whose density is known only up to a normalization constant.

• Recent breakthroughs in generative modeling to approximate a high-dimensional data distribution have sparked significant interest in developing neural network-based methods for this challenging problem.
• However, neural samplers typically incur heavy computational overhead due to simulating trajectories during training.
• This motivates the pursuit of simulation-free training procedures of neural samplers.