Planning in Dynamic Environments with Conditional Autoregressive Models
Nov 25, 2018
Johanna Hansen, Kyle Kastner, Aaron Courville, Gregory Dudek
Nov 25, 2018
Johanna Hansen, Kyle Kastner, Aaron Courville, Gregory Dudek


* 6 pages, 1 figure, in Proceedings of the Prediction and Generative Modeling in Reinforcement Learning Workshop at the International Conference on Machine Learning (ICML) in 2018
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Harmonic Recomposition using Conditional Autoregressive Modeling
Nov 18, 2018
Kyle Kastner, Rithesh Kumar, Tim Cooijmans, Aaron Courville
Nov 18, 2018
Kyle Kastner, Rithesh Kumar, Tim Cooijmans, Aaron Courville


* 3 pages, 2 figures. In Proceedings of The Joint Workshop on Machine Learning for Music, ICML 2018
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Learning Distributed Representations from Reviews for Collaborative Filtering
Jun 18, 2018
Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, Aaron Courville
Jun 18, 2018
Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, Aaron Courville




* Published in RecSys 2015 conference
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Learning to Discover Sparse Graphical Models
Aug 03, 2017
Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew Blaschko
We consider structure discovery of undirected graphical models from observational data. Inferring likely structures from few examples is a complex task often requiring the formulation of priors and sophisticated inference procedures. Popular methods rely on estimating a penalized maximum likelihood of the precision matrix. However, in these approaches structure recovery is an indirect consequence of the data-fit term, the penalty can be difficult to adapt for domain-specific knowledge, and the inference is computationally demanding. By contrast, it may be easier to generate training samples of data that arise from graphs with the desired structure properties. We propose here to leverage this latter source of information as training data to learn a function, parametrized by a neural network that maps empirical covariance matrices to estimated graph structures. Learning this function brings two benefits: it implicitly models the desired structure or sparsity properties to form suitable priors, and it can be tailored to the specific problem of edge structure discovery, rather than maximizing data likelihood. Applying this framework, we find our learnable graph-discovery method trained on synthetic data generalizes well: identifying relevant edges in both synthetic and real data, completely unknown at training time. We find that on genetics, brain imaging, and simulation data we obtain performance generally superior to analytical methods.
Aug 03, 2017
Eugene Belilovsky, Kyle Kastner, Gaël Varoquaux, Matthew Blaschko
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Representation Mixing for TTS Synthesis
Nov 24, 2018
Kyle Kastner, João Felipe Santos, Yoshua Bengio, Aaron Courville
Nov 24, 2018
Kyle Kastner, João Felipe Santos, Yoshua Bengio, Aaron Courville




* 5 pages, 3 figures
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Blindfold Baselines for Embodied QA
Nov 12, 2018
Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle, Aaron Courville
Nov 12, 2018
Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle, Aaron Courville



* NIPS 2018 Visually-Grounded Interaction and Language (ViGilL) Workshop
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A Recurrent Latent Variable Model for Sequential Data
Apr 06, 2016
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio
Apr 06, 2016
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, Yoshua Bengio




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ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks
Jul 23, 2015
Francesco Visin, Kyle Kastner, Kyunghyun Cho, Matteo Matteucci, Aaron Courville, Yoshua Bengio
Jul 23, 2015
Francesco Visin, Kyle Kastner, Kyunghyun Cho, Matteo Matteucci, Aaron Courville, Yoshua Bengio


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ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
May 24, 2016
Francesco Visin, Marco Ciccone, Adriana Romero, Kyle Kastner, Kyunghyun Cho, Yoshua Bengio, Matteo Matteucci, Aaron Courville
May 24, 2016
Francesco Visin, Marco Ciccone, Adriana Romero, Kyle Kastner, Kyunghyun Cho, Yoshua Bengio, Matteo Matteucci, Aaron Courville




* In CVPR Deep Vision Workshop, 2016
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