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Text Generation with Deep Variational GAN
Mahmoud Hossam, Trung Le, Michael Papasimeon +2
Generating realistic sequences is a central task in many machine learning applications. There has been considerable recent progress on building deep generative models for sequence…
Discrete-to-Deep Supervised Policy Learning
Budi Kurniawan, Peter Vamplew, Michael Papasimeon +2
Neural networks are effective function approximators, but hard to train in the reinforcement learning (RL) context mainly because samples are correlated. For years, scholars have g…
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation
Mahmoud Hossam, Trung Le, Viet Huynh +2
One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals. Current sequential generative models mainly gener…