Publications

Towards Text Generation with Adversarially Learned Neural Outlines.
Sandeep Subramanian
Sai Rajeswar
Alessandro Sordoni
Adam Trischler
Aaron C. Courville
Chris Pal
2017-12-31
Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (published)
dblp.uni-trier.de
Trends and Applications in Knowledge Discovery and Data Mining
Lida Rashidi
Benjamin C. M. Fung
Can Wang
2017-12-31
Lecture Notes in Computer Science (published)
doi.org
Twin Networks: Matching the Future for Sequence Generation
Dmitriy Serdyuk
Nan Rosemary Ke
Alessandro Sordoni
Adam Trischler
Christopher Pal
Yoshua Bengio
We propose a simple technique for encouraging generative RNNs to plan ahead. We train a "backward" recurrent network to generate a given seq… (see more)uence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model. The backward network is used only during training, and plays no role during sampling or inference. We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states). We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves significant improvement on a COCO caption generation task.
2017-12-31
ICLR (Poster) (published)
openreview.net
openreview.net
Universal Successor Representations for Transfer Reinforcement Learning
Chen Ma
Junfeng Wen
Yoshua Bengio
The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks. In this work, we focus o… (see more)n the transfer scenario where the dynamics among tasks are the same, but their goals differ. Although general value function (Sutton et al., 2011) has been shown to be useful for knowledge transfer, learning a universal value function can be challenging in practice. To attack this, we propose (1) to use universal successor representations (USR) to represent the transferable knowledge and (2) a USR approximator (USRA) that can be trained by interacting with the environment. Our experiments show that USR can be effectively applied to new tasks, and the agent initialized by the trained USRA can achieve the goal considerably faster than random initialization.
2017-12-31
ICLR (Workshop) (published)
openreview.net
openreview.net
Unsupervised Depth Estimation, 3D Face Rotation and Replacement
Joel Ruben Antony Moniz
Christopher Beckham
Simon Rajotte
Sina Honari
Christopher Pal
We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicti… (see more)ng 3D viewpoint transformations that match a desired pose and facial geometry. We achieve this by inferring the depth of facial keypoints of an input image in an unsupervised manner, without using any form of ground-truth depth information. We show how it is possible to use these depths as intermediate computations within a new backpropable loss to predict the parameters of a 3D affine transformation matrix that maps inferred 3D keypoints of an input face to the corresponding 2D keypoints on a desired target facial geometry or pose. Our resulting approach, called DepthNets, can therefore be used to infer plausible 3D transformations from one face pose to another, allowing faces to be frontalized, transformed into 3D models or even warped to another pose and facial geometry. Lastly, we identify certain shortcomings with our formulation, and explore adversarial image translation techniques as a post-processing step to re-synthesize complete head shots for faces re-targeted to different poses or identities.
2017-12-31
Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (published)
arxiv.org
arxiv.org
Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting
Di Wu
Boyu Wang
Doina Precup
Benoit Boulet
2017-12-29
Machine Learning and Knowledge Discovery in Databases (published)
doi.org
Dendritic error backpropagation in deep cortical microcircuits
João Sacramento
Rui Ponte Costa
Yoshua Bengio
Walter Senn
Animal behaviour depends on learning to associate sensory stimuli with the desired motor command. Understanding how the brain orchestrates t… (see more)he necessary synaptic modifications across different brain areas has remained a longstanding puzzle. Here, we introduce a multi-area neuronal network model in which synaptic plasticity continuously adapts the network towards a global desired output. In this model synaptic learning is driven by a local dendritic prediction error that arises from a failure to predict the top-down input given the bottom-up activities. Such errors occur at apical dendrites of pyramidal neurons where both long-range excitatory feedback and local inhibitory predictions are integrated. When local inhibition fails to match excitatory feedback an error occurs which triggers plasticity at bottom-up synapses at basal dendrites of the same pyramidal neurons. We demonstrate the learning capabilities of the model in a number of tasks and show that it approximates the classical error backpropagation algorithm. Finally, complementing this cortical circuit with a disinhibitory mechanism enables attention-like stimulus denoising and generation. Our framework makes several experimental predictions on the function of dendritic integration and cortical microcircuits, is consistent with recent observations of cross-area learning, and suggests a biological implementation of deep learning.
2017-12-29
ArXiv (preprint)
arxiv.org
arxiv.org
Tensor Regression Networks with various Low-Rank Tensor Approximations
Xingwei Cao
Guillaume Rabusseau
Joelle Pineau
Tensor regression networks achieve high compression rate of neural networks while having slight impact on performances. They do so by imposi… (see more)ng low tensor rank structure on the weight matrices of fully connected layers. In recent years, tensor regression networks have been investigated from the perspective of their compressive power, however, the regularization effect of enforcing low-rank tensor structure has not been investigated enough. We study tensor regression networks using various low-rank tensor approximations, aiming to compare the compressive and regularization power of different low-rank constraints. We evaluate the compressive and regularization performances of the proposed model with both deep and shallow convolutional neural networks. The outcome of our experiment suggests the superiority of Global Average Pooling Layer over Tensor Regression Layer when applied to deep convolutional neural network with CIFAR-10 dataset. On the contrary, shallow convolutional neural networks with tensor regression layer and dropout achieved lower test error than both Global Average Pooling and fully-connected layer with dropout function when trained with a small number of samples.
2017-12-26
ArXiv (preprint)
arxiv.org
arxiv.org
ObamaNet: Photo-realistic lip-sync from text
Rithesh Kumar
Jose Sotelo
Kundan Kumar
Alexandre De Brébisson
Yoshua Bengio
We present ObamaNet, the first architecture that generates both audio and synchronized photo-realistic lip-sync videos from any new text. Co… (see more)ntrary to other published lip-sync approaches, ours is only composed of fully trainable neural modules and does not rely on any traditional computer graphics methods. More precisely, we use three main modules: a text-to-speech network based on Char2Wav, a time-delayed LSTM to generate mouth-keypoints synced to the audio, and a network based on Pix2Pix to generate the video frames conditioned on the keypoints.
2017-12-05
ArXiv (preprint)
arxiv.org
arxiv.org
Deep Learning @15 Petaflops/second: Semi-supervised pattern detection for 15 Terabytes of climate data
W. Collins
M. Wehner
M. Prabhat
Thorsten Kurth
Nadathur Satish
Ioannis Mitliagkas
Jie Zhang
Evan Racah
Md. Mostofa Ali Patwary
Narayanan Sundaram
Pradeep Dubey
2017-11-30
(published)
www.semanticscholar.org
Predicting Extubation Readiness in Extreme Preterm Infants based on Patterns of Breathing*
Charles C. Onu
Lara J. Kanbar
Wissam Shalish
Karen A. Brown
Guilherme M. Sant'Anna
Robert E. Kearney
Doina Precup
Extremely preterm infants commonly require intubation and invasive mechanical ventilation after birth. While the duration of mechanical vent… (see more)ilation should be minimized in order to avoid complications, extubation failure is associated with increases in morbidities and mortality. As part of a prospective observational study aimed at developing an accurate predictor of extubation readiness, Markov and semi-Markov chain models were applied to gain insight into the respiratory patterns of these infants, with more robust time-series modeling using semi-Markov models. This model revealed interesting similarities and differences between newborns who succeeded extubation and those who failed. The parameters of the model were further applied to predict extubation readiness via generative (joint likelihood) and discriminative (support vector machine) approaches. Results showed that up to 84\% of infants who failed extubation could have been accurately identified prior to extubation.
2017-11-30
2017 IEEE Symposium Series on Computational Intelligence (SSCI) (published)
doi.org
arxiv.org
Use machine learning to find energy materials.
Phil De Luna
Jennifer N. Wei
Yoshua Bengio
Al'an Aspuru-guzik
E. Sargent
2017-11-30
Nature (published)
doi.org