Publications

A Semi-Markov Chain Approach to Modeling Respiratory Patterns Prior to Extubation in Preterm Infants

Charles C. Onu

Lara J. Kanbar

Wissam Shalish

Karen A. Brown

Guilherme M. Sant'Anna

Robert E. Kearney

After birth, extremely preterm infants often require specialized respiratory management in the form of invasive mechanical ventilation (IMV)… (voir plus). Protracted IMV is associated with detrimental outcomes and morbidities. Premature extubation, on the other hand, would necessitate reintubation which is risky, technically challenging and could further lead to lung injury or disease. We present an approach to modeling respiratory patterns of infants who succeeded extubation and those who required reintubation which relies on Markov models. We compare the use of traditional Markov chains to semi-Markov models which emphasize cross-pattern transitions and timing information, and to multi-chain Markov models which can concisely represent non-stationarity in respiratory behavior over time. The models we developed expose specific, unique similarities as well as vital differences between the two populations.

2017-07-10

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publié)

doi.org

arxiv.org

Multiscale sequence modeling with a learned dictionary

We propose a generalization of neural network sequence models. Instead of predicting one symbol at a time, our multi-scale model makes predi… (voir plus)ctions over multiple, potentially overlapping multi-symbol tokens. A variation of the byte-pair encoding (BPE) compression algorithm is used to learn the dictionary of tokens that the model is trained with. When applied to language modelling, our model has the flexibility of character-level models while maintaining many of the performance benefits of word-level models. Our experiments show that this model performs better than a regular LSTM on language modeling tasks, especially for smaller models.

2017-07-02

ArXiv (prépublication)

arxiv.org

Detecting Large Concept Extensions for Conceptual Analysis

L. Chartrand

Jackie CK Cheung

Mohamed Bouguessa

2017-07-01

Machine Learning and Data Mining in Pattern Recognition (publié)

doi.org

arxiv.org

Variance Regularizing Adversarial Learning

Karan Grewal

R Devon Hjelm

Yoshua Bengio

We study how, in generative adversarial networks, variance in the discriminator's output affects the generator's ability to learn the data d… (voir plus)istribution. In particular, we contrast the results from various well-known techniques for training GANs when the discriminator is near-optimal and updated multiple times per update to the generator. As an alternative, we propose an additional method to train GANs by explicitly modeling the discriminator's output as a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target bi-modal distribution implicitly through meta-adversarial training. We observe that our new method, when trained together with a strong discriminator, provides meaningful, non-vanishing gradients.

2017-07-01

ArXiv (prépublication)

openreview.net

APEX_SCOPE: A graphical user interface for visualization of multi-modal data in inter-disciplinary studies.

Lara J. Kanbar

Wissam Shalish

Doina Precup

Karen A. Brown

Guilherme M. Sant'Anna

Robert E. Kearney

2017-06-30

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (publié)

doi.org

Towards more hardware-friendly deep learning

Yoshua Bengio

2017-06-23

TiML '17 (publié)

doi.org

Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control

Policy gradient methods in reinforcement learning have become increasingly prevalent for state-of-the-art performance in continuous control … (voir plus)tasks. Novel methods typically benchmark against a few key algorithms such as deep deterministic policy gradients and trust region policy optimization. As such, it is important to present and use consistent baselines experiments. However, this can be difficult due to general variance in the algorithms, hyper-parameter tuning, and environment stochasticity. We investigate and discuss: the significance of hyper-parameters in policy gradients for continuous control, general variance in the algorithms, and reproducibility of reported results. We provide guidelines on reporting novel results as comparisons against baseline methods such that future researchers can make informed decisions when investigating novel methods.

2017-06-16

ICML.cc/2017/RML (poster)

openreview.net

Time-Varying Mixtures of Markov Chains: An Application to Road Traffic Modeling

Sean Lawlor

Michael G. Rabbat

Time-varying mixture models are useful for representing complex, dynamic distributions. Components in the mixture model can appear and disap… (voir plus)pear, and persisting components can evolve. This allows great flexibility in streaming data applications where the model can be adjusted as new data arrives. Fitting a mixture model with computational guarantees which can meet real-time requirements is challenging with existing algorithms, especially when the model order can vary with time. Existing approximate inference methods may require multiple restarts to search for a good local solution. Monte-Carlo methods can be used to jointly estimate the model order and model parameters, but when the distribution of each mixand has a high-dimensional parameter space, they suffer from the curse of dimensionality and and from slow convergence. This paper proposes a generative model for time-varying mixture models, tailored for mixtures of discrete-time Markov chains. A novel, deterministic inference procedure is introduced and is shown to be suitable for applications requiring real-time estimation, and the method is guaranteed to converge at each time step. As a motivating application, we model and predict traffic patterns in a transportation network. Experiments illustrate the performance of the scheme and offer insights regarding tuning of the algorithm parameters. The experiments also investigate the predictive power of the proposed model compared to less complex models and demonstrate the superiority of the mixture model approach for prediction of traffic routes in real data.

2017-06-14

IEEE Transactions on Signal Processing (publié)

doi.org

Learning to Compute Word Embeddings on the Fly

Dzmitry Bahdanau

Tom Bosc

Stanisław Jastrzębski

Edward Grefenstette

Pascal Vincent

Yoshua Bengio

Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for word… (voir plus)s in the "long tail" of this distribution requires enormous amounts of data. Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-train embeddings on external data, or treat all rare words as out-of-vocabulary words with a unique representation. We provide a method for predicting embeddings of rare words on the fly from small amounts of auxiliary data with a network trained end-to-end for the downstream task. We show that this improves results against baselines where embeddings are trained on the end task for reading comprehension, recognizing textual entailment and language modeling.

2017-05-31

arXiv (prépublication)

doi.org

openreview.net

Deep Learning for Patient-Specific Kidney Graft Survival Analysis

Margaux Luck

Tristan Sylvain

Heloise Cardinal

Andrea Lodi

Yoshua Bengio

An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and ca… (voir plus)re of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for graft patients based on the principle of multi-task learning. By learning to jointly predict the time of the event, and its rank in the cox partial log likelihood framework, our deep learning approach outperforms, in terms of survival time prediction quality and concordance index, other common methods for survival analysis, including the Cox Proportional Hazards model and a network trained on the cox partial log-likelihood.

2017-05-28

ArXiv (prépublication)

arxiv.org

Deep Complex Networks

Christopher Pal

2017-05-26

ArXiv (prépublication)

openreview.net

Implementation of Sparse Superposition Codes

Carlo Condo

Warren J. Gross

Sparse superposition codes (SSCs) are capacity achieving codes whose decoding process is a linear sensing problem. Decoding approaches thus … (voir plus)exploit the approximate message passing algorithm, which has been proven to be effective in compressing sensing. Previous work from the authors has evaluated the error correction performance of SSCs under finite precision and finite code length. This paper proposes the first SSC encoder and decoder architectures in the literature. The architectures are parametrized and applicable to all SSCs: A set of wide-ranging case studies is then considered, and code-specific approximations, along with implementation results in 65 nm CMOS technology, are then provided. The encoding process can be carried out with low power consumption (≤2.103 mW), while the semi-parallel decoder architecture can reach a throughput of 1.3 Gb/s with a 768 × 6-bit SSC codeword and an area occupation of 2.43 mm2.

2017-04-30

IEEE Transactions on Signal Processing (publié)

doi.org

Mila sur Udemy

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

Publications

Mila sur Udemy

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

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