Deep Sequence Models: Learning to Generate Data and Adversarial Attacks

Understanding sequential data like natural language sentences and learning to model it with generative models are fundamental research problems in artificial intelligence. Solving them helps to create machines that are imaginative and which can perform human-like reasoning and robust decision making. Advanced sequence models will have a significant impact on key areas including drug discovery, autonomous vehicles, and robotics. This thesis advances research in sequence models in two ways: by introducing controlling mechanisms into generative models, and by learning to efficiently generate attacks on natural language models.