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Institution

École Polytechnique de Montréal

EducationMontreal, Quebec, Canada
About: École Polytechnique de Montréal is a(n) education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topic(s): Finite element method & Population. The organization has 8015 authors who have published 18390 publication(s) receiving 494372 citation(s).
Papers
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Journal ArticleDOI
Yann LeCun1, Léon Bottou2, Léon Bottou3, Yoshua Bengio3  +3 moreInstitutions (5)
01 Jan 1998-
Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

34,930 citations


Proceedings ArticleDOI
01 Jan 2014-
Abstract: In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder‐Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.

14,140 citations


Posted Content
TL;DR: These advanced recurrent units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU), are found to be comparable to LSTM.
Abstract: In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.

7,349 citations


Proceedings ArticleDOI
03 Sep 2014-
Abstract: Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks. The neural machine translation models often consist of an encoder and a decoder. The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation. In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder‐Decoder and a newly proposed gated recursive convolutional neural network. We show that the neural machine translation performs relatively well on short sentences without unknown words, but its performance degrades rapidly as the length of the sentence and the number of unknown words increase. Furthermore, we find that the proposed gated recursive convolutional network learns a grammatical structure of a sentence automatically.

3,794 citations


Book
01 Oct 1998-

3,640 citations


Authors

Showing all 8015 results

NameH-indexPapersCitations
Yoshua Bengio2021033420313
Claude Leroy135117088604
Lucie Gauthier13267964794
Reyhaneh Rezvani12063861776
M. Giunta11560866189
Alain Dufresne11135845904
David Brown105125746827
Pierre Legendre9836682995
Michel Bouvier9739631267
Aharon Gedanken9686138974
Michel Gendreau9445636253
Frederick Dallaire9347531049
Pierre Savard9342742186
Nader Engheta8961935204
Ke Wu87124233226
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202238
20211,270
20201,207
20191,140
20181,102
20171,077

Top Attributes

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Institution's top 5 most impactful journals

IFAC Proceedings Volumes

103 papers, 646 citations

Optics Express

86 papers, 3.2K citations