Athul Paul Jacob

TL;DR: This work introduces several ways of regularizing the objective, which can dramatically stabilize the training of GAN models and shows that these regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.

TL;DR: This work introduces a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.

TL;DR: In this paper, the authors introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models and also show that their regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training.

TL;DR: The authors proposed a constituency parsing scheme, which predicts a real-valued scalar, named syntactic distance, for each split position in the sentence and the topology of grammar tree is then determined by the values of syntactic distances.

TL;DR: Cicero as mentioned in this paper is the first AI agent to achieve human-level performance in Diplomacy, a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players.