Search Results for author:
Found 5 papers, 3 papers with code
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
In this paper, we show that it is possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes and simple training techniques.
A Deep Reinforcement Learning Chatbot (Short Version)
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition.
ObamaNet: Photo-realistic lip-sync from text
We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text.
A Deep Reinforcement Learning Chatbot
By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble.
Deep Neural Networks for Anatomical Brain Segmentation
To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks.
