Applications of time-series generative models and inference techniques - Teng, M (2022)
In this dissertation, we apply deep generative modelling, amortised inference and reinforcement learning methods to real-world, practical phenomenon, and we ask if these techniques can be used to predict complex system dynamics, model biologically plausible behaviour, and guide decision making. In the past, probabilistic modelling and Bayesian inference techniques have been successfully applied in a wide array of fields, achieving success in financial market prediction, robotics, and the natural sciences. However, the use of generative models in these contexts has usually required a rigid set of linearity constraints or assumptions about the distributions used for modelling. Furthermore, inference in non-linear models can be very difficult to scale to high-dimensional models. In recent years, deep learning has been a key innovation in enabling non-linear function approximation. When applied to probabilistic modelling, deep non-linear models have significantly improved the generative capabilities of computer vision models. While an important step towards general artificial intelligence, there remains a gap between the successes of these early single-time-step deep generative models and the temporal models that will be required to deploy machine learning in the real-world.