Publications

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… (see more)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.

2017-11-15

International Journal of Computer Applications (published)

Variational Bi-LSTMs

Samira Shabanian

Devansh Arpit

Adam Trischler

Recurrent neural networks like long short-term memory (LSTM) are important architectures for sequential prediction tasks. LSTMs (and RNNs in… (see more) general) model sequences along the forward time direction. Bidirectional LSTMs (Bi-LSTMs), which model sequences along both forward and backward directions, generally perform better at such tasks because they capture a richer representation of the data. In the training of Bi-LSTMs, the forward and backward paths are learned independently. We propose a variant of the Bi-LSTM architecture, which we call Variational Bi-LSTM, that creates a dependence between the two paths (during training, but which may be omitted during inference). Our model acts as a regularizer and encourages the two networks to inform each other in making their respective predictions using distinct information. We perform ablation studies to better understand the different components of our model and evaluate the method on various benchmarks, showing state-of-the-art performance.

2017-11-15

ArXiv (preprint)

Variational Bi-LSTMs

Samira Shabanian

Devansh Arpit

Adam Trischler

2017-11-15

ArXiv (preprint)

ACtuAL: Actor-Critic Under Adversarial Learning

Anirudh Goyal

Nan Rosemary Ke

Alex Lamb

Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs ar… (see more)e typically trained end-to-end on real-valued data and can be used to train a generator of high-dimensional and realistic images. However, a major limitation of GANs is that training relies on passing gradients from the discriminator through the generator via back-propagation. This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function. These difficulties extend to the reinforcement learning setting when the action space is composed of discrete decisions. We address these issues by reframing the GAN framework so that the generator is no longer trained using gradients through the discriminator, but is instead trained using a learned critic in the actor-critic framework with a Temporal Difference (TD) objective. This is a natural fit for sequence modeling and we use it to achieve improvements on language modeling tasks over the standard Teacher-Forcing methods.

2017-11-13

ArXiv (preprint)

Three Factors Influencing Minima in SGD

Stanisław Jastrzębski

Zac Kenton

Devansh Arpit

Nicolas Ballas

Asja Fischer

Amos Storkey

We study the statistical properties of the endpoint of stochastic gradient descent (SGD). We approximate SGD as a stochastic differential eq… (see more)uation (SDE) and consider its Boltzmann Gibbs equilibrium distribution under the assumption of isotropic variance in loss gradients.. Through this analysis, we find that three factors – learning rate, batch size and the variance of the loss gradients – control the trade-off between the depth and width of the minima found by SGD, with wider minima favoured by a higher ratio of learning rate to batch size. In the equilibrium distribution only the ratio of learning rate to batch size appears, implying that it’s invariant under a simultaneous rescaling of each by the same amount. We experimentally show how learning rate and batch size affect SGD from two perspectives: the endpoint of SGD and the dynamics that lead up to it. For the endpoint, the experiments suggest the endpoint of SGD is similar under simultaneous rescaling of batch size and learning rate, and also that a higher ratio leads to flatter minima, both findings are consistent with our theoretical analysis. We note experimentally that the dynamics also seem to be similar under the same rescaling of learning rate and batch size, which we explore showing that one can exchange batch size and learning rate in a cyclical learning rate schedule. Next, we illustrate how noise affects memorization, showing that high noise levels lead to better generalization. Finally, we find experimentally that the similarity under simultaneous rescaling of learning rate and batch size breaks down if the learning rate gets too large or the batch size gets too small.

2017-11-13

ArXiv (preprint)

openreview.net

Fast and Flexible Successive-Cancellation List Decoders for Polar Codes

Seyyed Ali Hashemi

Carlo Condo

Warren Gross

Polar codes have gained significant amount of attention during the past few years and have been selected as a coding scheme for the next gen… (see more)eration of mobile broadband standard. Among decoding schemes, successive-cancellation list (SCL) decoding provides a reasonable tradeoff between the error-correction performance and hardware implementation complexity when used to decode polar codes, at the cost of limited throughput. The simplified SCL (SSCL) and its extension SSCL-SPC increase the speed of decoding by removing redundant calculations when encountering particular information and frozen bit patterns (rate one and single parity check codes), while keeping the error-correction performance unaltered. In this paper, we improve SSCL and SSCL-SPC by proving that the list size imposes a specific number of path splitting required to decode rate one and single parity check codes. Thus, the number of splitting can be limited while guaranteeing exactly the same error-correction performance as if the paths were forked at each bit estimation. We call the new decoding algorithms Fast-SSCL and Fast-SSCL-SPC. Moreover, we show that the number of path forks in a practical application can be tuned to achieve desirable speed, while keeping the error-correction performance almost unchanged. Hardware architectures implementing both algorithms are then described and implemented: It is shown that our design can achieve

2017-11-01

IEEE Transactions on Signal Processing (published)

Fraternal Dropout

Konrad Żołna

Devansh Arpit

Dendi Suhubdy

2017-10-31

ArXiv (preprint)

Graph Attention Networks

Petar Veličković

Guillem Cucurull

Arantxa Casanova

Adriana Romero Soriano

Pietro Lio

2017-10-30

ArXiv (preprint)

Graph Attention Networks

Petar Veličković

Guillem Cucurull

Arantxa Casanova

Adriana Romero Soriano

Pietro Lio

2017-10-30

ArXiv (preprint)

Automatic Differentiation in Myia

Olivier Breuleux

Bart van Merriënboer

Automatic differentiation is an essential feature of machine learning frameworks. However, its implementation in existing frameworks often h… (see more)as limitations. In dataflow programming frameworks such as Theano or TensorFlow the representation used makes supporting higher-order gradients difficult. On the other hand, operator overloading frameworks such as PyTorch are flexible, but do not lend themselves well to optimization. With Myia, we attempt to have the best of both worlds: Building on the work by Pearlmutter and Siskind we implement a first-order gradient operator for a subset of the Python programming language.

2017-10-28

(published)

www.semanticscholar.org

A Multisensor Multi-Bernoulli Filter

Augustin-Alexandru Saucan

Mark Coates

Michael Rabbat

In this paper, we derive a multisensor multi-Bernoulli (MS-MeMBer) filter for multitarget tracking. Measurements from multiple sensors are e… (see more)mployed by the proposed filter to update a set of tracks modeled as a multi-Bernoulli random finite set. An exact implementation of the MS-MeMBer update procedure is computationally intractable. We propose an efficient approximate implementation by using a greedy measurement partitioning mechanism. The proposed filter allows for Gaussian mixture or particle filter implementations. Numerical simulations conducted for both linear-Gaussian and nonlinear models highlight the improved accuracy of the MS-MeMBer filter and its reduced computational load with respect to the multisensor cardinalized probability hypothesis density filter and the iterated-corrector cardinality-balanced multi-Bernoulli filter especially for low probabilities of detection.

2017-10-15

IEEE Transactions on Signal Processing (published)

Bayesian Hypernetworks

David Scott Krueger

Chin-Wei Huang

Riashat Islam

Ryan Turner

Alexandre Lacoste

Aaron Courville

2017-10-13

ArXiv (preprint)