Generative Restricted Kernel Machines

Generative modeling is a rapidly advancing area of machine learning research finding applications in multiple fields such as, generated art, on-demand video, image denoising [1], exploration in reinforcement learning [2], collaborative filtering [3], inpainting [4] and many more. In general, three approaches have been used in generative modeling tasks. First, graphical models based on a probabilistic framework with latent variables such as variational auto-encoders [5] and Restricted Boltzmann Machines (RBMs) [6, 7]. Then, more recently proposed models based on adversarial training such as Generative Adversarial Networks (GANs) [8] and its many variants. Furthermore, autoregressive models such as Pixel Recurrent Neural Networks (PixelRNNs) [9] that models the conditional distribution of every individual pixel given previous pixels and generation involves sequentially predicting the pixels in an image along the two spatial dimensions.