Generative Neural Networks: Sampling and Training

This lecture introduces the concept of generative neural networks, focusing on the process of sampling and training. The instructor explains how to add noise to samples, train a neural network to denoise them, and generate new samples. The lecture covers constructing a flow to transform noise into samples, defining interpolants, and discussing properties of the interpolants. The backward process is also explored, where an interpolator is defined to generate samples from noise. The relationship between the forward and backward processes is analyzed, emphasizing the importance of understanding the speed parameter in generating samples. The lecture concludes with a discussion on how to compute the speed parameter, crucial for generating samples when the target distribution is unknown.