599 citations · 1.3k across the 7 of their papers we have counts for
7 papers
PoDAR: Power-Decoupled Audio Representation for Generative Modeling
Alejandro Luebs, Mithilesh Vaidya, Ishaan Kumar +5
The performance of audio latent diffusion models is primarily governed by generator expressivity and the modelability of the underlying latent space. While recent research has focu…
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Kundan Kumar, Rithesh Kumar, Thibault de Boissiere +6
Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms with GANs is challenging. In this paper, we show that it is poss…
A Deep Reinforcement Learning Chatbot (Short Version)
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…
ObamaNet: Photo-realistic lip-sync from text
Rithesh Kumar, Jose Sotelo, Kundan Kumar +2
We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Contrary to other published lip-sync appro…
A Deep Reinforcement Learning Chatbot
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…
A Robust Adaptive Stochastic Gradient Method for Deep Learning
Caglar Gulcehre, Jose Sotelo, Marcin Moczulski +1
Stochastic gradient algorithms are the main focus of large-scale optimization problems and led to important successes in the recent advancement of the deep learning algorithms. The…
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
Soroush Mehri, Kundan Kumar, Ishaan Gulrajani +5
In this paper we propose a novel model for unconditional audio generation based on generating one audio sample at a time. We show that our model, which profits from combining memor…