Publications

Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms
Kian Kenyon-Dean
Jackie CK Cheung
Doina Precup
Verb Phrase Ellipsis (VPE) is an anaphoric construction in which a verb phrase has been elided. It occurs frequently in dialogue and informa… (voir plus)l conversational settings, but despite its evident impact on event coreference resolution and extraction, there has been relatively little work on computational methods for identifying and resolving VPE. Here, we present a novel approach to detecting and resolving VPE by using supervised discriminative machine learning techniques trained on features extracted from an automatically parsed, publicly available dataset. Our approach yields state-of-the-art results for VPE detection by improving F1 score by over 11%; additionally, we explore an approach to antecedent identifi-cation that uses the Margin-Infused-Relaxed-Algorithm, which shows promising results.
2016-10-31
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
HeMIS: Hetero-Modal Image Segmentation
Mohammad Havaei
Nicolas Guizard
Nicolas Chapados
Yoshua Bengio
2016-10-01
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 (publié)
doi.org
arxiv.org
Prediction of Cell Type Specific Transcription Factor Binding Site Occupancy
Faizy Ahsan
Doina Precup
Mathieu Blanchette
2016-10-01
ACM International Conference on Bioinformatics, Computational Biology and Biomedicine (publié)
doi.org
Towards End-to-End Speech Recognition with Deep Convolutional Neural Networks
Ying Zhang
Mohammad Pezeshki
Philemon Brakel
Saizheng Zhang
César Laurent
Yoshua Bengio
Aaron Courville
Convolutional Neural Networks (CNNs) are effective models for reducing spectral variations and modeling spectral correlations in acoustic fe… (voir plus)atures for automatic speech recognition (ASR). Hybrid speech recognition systems incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models (HMMs/GMMs) have achieved the state-of-the-art in various benchmarks. Meanwhile, Connectionist Temporal Classification (CTC) with Recurrent Neural Networks (RNNs), which is proposed for labeling unsegmented sequences, makes it feasible to train an end-to-end speech recognition system instead of hybrid settings. However, RNNs are computationally expensive and sometimes difficult to train. In this paper, inspired by the advantages of both CNNs and the CTC approach, we propose an end-to-end speech framework for sequence labeling, by combining hierarchical CNNs with CTC directly without recurrent connections. By evaluating the approach on the TIMIT phoneme recognition task, we show that the proposed model is not only computationally efficient, but also competitive with the existing baseline systems. Moreover, we argue that CNNs have the capability to model temporal correlations with appropriate context information.
2016-09-07
Interspeech 2016 (publié)
doi.org
arxiv.org
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Iulian V. Serban
Alberto García-Durán
Caglar Gulçehre
Sungjin Ahn
A. Chandar
Aaron Courville
Yoshua Bengio
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. Howeve… (voir plus)r, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.
2016-07-31
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
doi.org
arxiv.org
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
We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and th… (voir plus)e capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally and vertically in both directions, encoding patches or activations, and providing relevant global information. Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features. Upsampling layers follow ReNet layers to recover the original image resolution in the final predictions. The proposed ReSeg architecture is efficient, flexible and suitable for a variety of semantic segmentation tasks. We evaluate ReSeg on several widely-used semantic segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving state-of-the-art performance. Results show that ReSeg can act as a suitable architecture for semantic segmentation tasks, and may have further applications in other structured prediction problems. The source code and model hyperparameters are available on https://github.com/fvisin/reseg.
2016-06-30
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)
doi.org
arxiv.org
Deconstructing the Ladder Network Architecture
Mohammad Pezeshki
Linxi Fan
Philemon Brakel
Aaron Courville
Yoshua Bengio
The Manual labeling of data is and will remain a costly endeavor. For this reason, semi-supervised learning remains a topic of practical imp… (voir plus)ortance. The recently proposed Ladder Network is one such approach that has proven to be very successful. In addition to the supervised objective, the Ladder Network also adds an unsupervised objective corresponding to the reconstruction costs of a stack of denoising autoencoders. Although the empirical results are impressive, the Ladder Network has many components intertwined, whose contributions are not obvious in such a complex architecture. In order to help elucidate and disentangle the different ingredients in the Ladder Network recipe, this paper presents an extensive experimental investigation of variants of the Ladder Network in which we replace or remove individual components to gain more insight into their relative importance. We find that all of the components are necessary for achieving optimal performance, but they do not contribute equally. For semi-supervised tasks, we conclude that the most important contribution is made by the lateral connection, followed by the application of noise, and finally the choice of what we refer to as the `combinator function' in the decoder path. We also find that as the number of labeled training examples increases, the lateral connections and reconstruction criterion become less important, with most of the improvement in generalization being due to the injection of noise in each layer. Furthermore, we present a new type of combinator function that outperforms the original design in both fully- and semi-supervised tasks, reducing record test error rates on Permutation-Invariant MNIST to 0.57% for the supervised setting, and to 0.97% and 1.0% for semi-supervised settings with 1000 and 100 labeled examples respectively.
2016-06-10
Proceedings of The 33rd International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Dynamic Capacity Networks
Amjad Almahairi
Nicolas Ballas
Tim Cooijmans
Yin Zheng
Hugo Larochelle
Aaron Courville
We introduce the Dynamic Capacity Network (DCN), a neural network that can adaptively assign its capacity across different portions of the i… (voir plus)nput data. This is achieved by combining modules of two types: low-capacity sub-networks and high-capacity sub-networks. The low-capacity sub-networks are applied across most of the input, but also provide a guide to select a few portions of the input on which to apply the high-capacity sub-networks. The selection is made using a novel gradient-based attention mechanism, that efficiently identifies input regions for which the DCN's output is most sensitive and to which we should devote more capacity. We focus our empirical evaluation on the Cluttered MNIST and SVHN image datasets. Our findings indicate that DCNs are able to drastically reduce the number of computations, compared to traditional convolutional neural networks, while maintaining similar or even better performance.
2016-06-10
Proceedings of The 33rd International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
First Result on Arabic Neural Machine Translation
Amjad Almahairi
Kyunghyun Cho
Nizar Habash
Aaron Courville
Neural machine translation has become a major alternative to widely used phrase-based statistical machine translation. We notice however tha… (voir plus)t much of research on neural machine translation has focused on European languages despite its language agnostic nature. In this paper, we apply neural machine translation to the task of Arabic translation (Ar En) and compare it against a standard phrase-based translation system. We run extensive comparison using various configurations in preprocessing Arabic script and show that the phrase-based and neural translation systems perform comparably to each other and that proper preprocessing of Arabic script has a similar effect on both of the systems. We however observe that the neural machine translation significantly outperform the phrase-based system on an out-of-domain test set, making it attractive for real-world deployment.
2016-06-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Brain Tumor Segmentation with Deep Neural Networks
Mohammad Havaei
Axel Davy
David Warde-Farley
Antoine Biard
Aaron Courville
Yoshua Bengio
Chris Pal
Pierre-Marc Jodoin
Hugo Larochelle
2016-05-20
Medical Image Analysis (inconnu)
doi.org
arxiv.org
Movie Description
Anna Rohrbach
Atousa Torabi
Marcus Rohrbach
Niket Tandon
Christopher Pal
Hugo Larochelle
Aaron Courville
Bernt Schiele
Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their pee… (voir plus)rs. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.
2016-05-11
ArXiv (prépublication)
doi.org
arxiv.org
Nash equilibria in the two-player kidney exchange game
Margarida Carvalho
Andrea Lodi
João Pedro Pedroso
Ana Luiza D'ávila Viana
2016-04-20
Mathematical Programming (publié)
doi.org