K
Kyle Kastner
Researcher at Université de Montréal
Publications - 28
Citations - 2873
Kyle Kastner is an academic researcher from Université de Montréal. The author has contributed to research in topics: Recurrent neural network & Artificial neural network. The author has an hindex of 14, co-authored 24 publications receiving 2402 citations. Previous affiliations of Kyle Kastner include Salk Institute for Biological Studies.
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A Recurrent Latent Variable Model for Sequential Data
TL;DR: In this article, the authors explore the use of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder.
Proceedings Article
A recurrent latent variable model for sequential data
TL;DR: It is argued that through the use of high-level latent random variables, the variational RNN (VRNN)1 can model the kind of variability observed in highly structured sequential data such as natural speech.
Proceedings Article
Char2Wav: End-to-End Speech Synthesis
Jose Sotelo,Soroush Mehri,Kundan Kumar,João Felipe Santos,Kyle Kastner,Aaron Courville,Yoshua Bengio +6 more
TL;DR: Char2Wav is an end-to-end model for speech synthesis that learns to produce audio directly from text and is a bidirectional recurrent neural network with attention that produces vocoder acoustic features.
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ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks.
TL;DR: The proposed network, called ReNet, replaces the ubiquitous convolution+pooling layer of the deep convolutional neural network with four recurrent neural networks that sweep horizontally and vertically in both directions across the image.
Proceedings ArticleDOI
ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation
Francesco Visin,Adriana Romero,Kyunghyun Cho,Matteo Matteucci,Marco Ciccone,Kyle Kastner,Yoshua Bengio,Aaron Courville +7 more
TL;DR: In this article, the authors proposed a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies.