Publications

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… (voir plus)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) (publié)
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 (publié)
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… (voir plus)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 (prépublication)
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… (voir plus)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 (prépublication)
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… (voir plus)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 (prépublication)
arxiv.org
arxiv.org
Use machine learning to find energy materials.
Phil De Luna
Jennifer N. Wei
Yoshua Bengio
Al'an Aspuru-guzik
E. Sargent
2017-12-05
Nature (publié)
doi.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
(publié)
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… (voir plus)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) (publié)
doi.org
arxiv.org
Learnings Options End-to-End for Continuous Action Tasks
Martin Klissarov
Pierre-Luc Bacon
Jean Harb
Doina Precup
We present new results on learning temporally extended actions for continuoustasks, using the options framework (Suttonet al.[1999b], Precup… (voir plus) [2000]). In orderto achieve this goal we work with the option-critic architecture (Baconet al.[2017])using a deliberation cost and train it with proximal policy optimization (Schulmanet al.[2017]) instead of vanilla policy gradient. Results on Mujoco domains arepromising, but lead to interesting questions aboutwhena given option should beused, an issue directly connected to the use of initiation sets.
2017-11-29
ArXiv (prépublication)
arxiv.org
arxiv.org
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
Jason Jo
Yoshua Bengio
Deep CNNs are known to exhibit the following peculiarity: on the one hand they generalize extremely well to a test set, while on the other h… (voir plus)and they are extremely sensitive to so-called adversarial perturbations. The extreme sensitivity of high performance CNNs to adversarial examples casts serious doubt that these networks are learning high level abstractions in the dataset. We are concerned with the following question: How can a deep CNN that does not learn any high level semantics of the dataset manage to generalize so well? The goal of this article is to measure the tendency of CNNs to learn surface statistical regularities of the dataset. To this end, we use Fourier filtering to construct datasets which share the exact same high level abstractions but exhibit qualitatively different surface statistical regularities. For the SVHN and CIFAR-10 datasets, we present two Fourier filtered variants: a low frequency variant and a randomly filtered variant. Each of the Fourier filtering schemes is tuned to preserve the recognizability of the objects. Our main finding is that CNNs exhibit a tendency to latch onto the Fourier image statistics of the training dataset, sometimes exhibiting up to a 28% generalization gap across the various test sets. Moreover, we observe that significantly increasing the depth of a network has a very marginal impact on closing the aforementioned generalization gap. Thus we provide quantitative evidence supporting the hypothesis that deep CNNs tend to learn surface statistical regularities in the dataset rather than higher-level abstract concepts.
2017-11-29
ArXiv (prépublication)
arxiv.org
arxiv.org
Ubenwa: Cry-based Diagnosis of Birth Asphyxia
Charles Onu
Innocent Udeogu
Eyenimi Ndiomu
Urbain Kengni
Doina Precup
Guilherme M. Sant’Anna
E. Alikor
P. Opara
Every year, 3 million newborns die within the first month of life. Birth asphyxia and other breathing-related conditions are a leading cause… (voir plus) of mortality during the neonatal phase. Current diagnostic methods are too sophisticated in terms of equipment, required expertise, and general logistics. Consequently, early detection of asphyxia in newborns is very difficult in many parts of the world, especially in resource-poor settings. We are developing a machine learning system, dubbed Ubenwa, which enables diagnosis of asphyxia through automated analysis of the infant cry. Deployed via smartphone and wearable technology, Ubenwa will drastically reduce the time, cost and skill required to make accurate and potentially life-saving diagnoses.
2017-11-16
ArXiv (prépublication)
arxiv.org
arxiv.org
Design of a Recognition System Automatic Vehicle License Plate through a Convolution Neural Network
P. Rajendra
K. Sudheer
Rahul Boadh
TE Campos
BR Babu
M. Varma
Ian J Goodfellow
Yoshua Bengio
Aaron
The present work is a study on the practical application of Learning process (Deep Learning) in the development of a system of Automatic rec… (voir plus)ognition of vehicle license plates. These systems commonly referred to as ALPR (Automatic License Plate Recognition) - are able to recognize the content of vehicles from the images captured by a camera. The system proposed in this work is based on an image classifier developed through supervised learning techniques with convolution neural network. These networks are one of the most profound learning architectures and are specifically designed to solve artificial vision, such as pattern recognition and classification of images. This paper also examines basic processing techniques and Image segmentation - such as smoothing filters, contour detection - necessary for the proposed system to be able to extract the contents of the license plates for further analysis and classification. This paper demonstrates the feasibility of an ALPR system based on a convolution neural network, noting the critical importance it has to design a network architecture and training data set appropriate to the problem to be solved.
2017-11-14
International Journal of Computer Applications (publié)
doi.org