This research is driven by our curiosity about the limits to which the current technology for natural language processing could be pushed: if humans are capable of reading jumbled sentences, can machines do? To answer this question, we evaluate the ability of state-of-the-art models to recognize and comprehend wordand character-level... [Read More]

This research project studies a novel online decision-making problem of choosing the correct action each round given access to experts that are knowledgeable, rational, and truthful, rather than arbitrary. We ask, in this setting, whether we can surpass the best expert using voting-based algorithms when the experts provide additional information... [Read More]

We propose a novel approach of imitation refinement, which improves the quality of imperfect patterns, by mimicking the characteristics of ideal data. We demonstrate the effectiveness of imitation refinement on two real-world applications: in the first application, we show imitation refinement improves the quality of poorly written digits by imitating... [Read More]

We propose a novel framework for the problem of minimizing domain expert annotation time for image segmentation using two adversarial and complementary multi-armed agents. Specifically, the framework contains two reinforcement agents where one’s goal is to reinforce the segmentation model’s current knowledge, and the others goal is to exploit the... [Read More]

DeepMind researchers claimed state-of-the-art results in the Arcade Learning Environment in their recent ICML paper “A Distributional Perspective on Reinforcement Learning”. They investigate a distributional perspective of the value function in the reinforcement learning context and further design an algorithm applying Bellman’s equation to approximately learn value distributions, which results... [Read More]