Alexandre de Brébisson — accepted AI-conference papers

2019

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo +3

NeurIPS 2019poster

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 possible to train GANs reliably to generate high quality coherent waveforms by introducing a set of architectural changes an…

Cited by 1249 Source ↗PDF ↗code ↗Scholar ↗

2015

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier

NeurIPS 2015oral

An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e.g. neural language models or the learning of word-embeddings, often posed as predicting the probability of next words among a vocabulary of size D…

Cited by 67 Source ↗PDF ↗code ↗Scholar ↗