Publications

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to… (see more) perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss rate and the inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing decision support systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endoluminal scene, targeting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCNs). We perform a comparative study to show that FCNs significantly outperform, without any further postprocessing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization.

2017-07-26

Journal of Healthcare Engineering (published)

Self-organized Hierarchical Softmax

Yikang Shen

Shawn Tan

Chris Pal

We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead … (see more)of using a predefined hierarchical structure, our approach is capable of learning word clusters with clear syntactical and semantic meaning during the language model training process. We provide experiments on standard benchmarks for language modeling and sentence compression tasks. We find that this approach is as fast as other efficient softmax approximations, while achieving comparable or even better performance relative to similar full softmax models.

2017-07-26

ArXiv (preprint)

A Dataset and Exploration of Models for Understanding Video Data through Fill-in-the-Blank Question-Answering

Tegan Maharaj

Nicolas Ballas

Anna Rohrbach

Chris Pal

While deep convolutional neural networks frequently approach or exceed human-level performance in benchmark tasks involving static images, e… (see more)xtending this success to moving images is not straightforward. Video understanding is of interest for many applications, including content recommendation, prediction, summarization, event/object detection, and understanding human visual perception. However, many domains lack sufficient data to explore and perfect video models. In order to address the need for a simple, quantitative benchmark for developing and understanding video, we present MovieFIB, a fill-in-the-blank question-answering dataset with over 300,000 examples, based on descriptive video annotations for the visually impaired. In addition to presenting statistics and a description of the dataset, we perform a detailed analysis of 5 different models predictions, and compare these with human performance. We investigate the relative importance of language, static (2D) visual features, and moving (3D) visual features, the effects of increasing dataset size, the number of frames sampled, and of vocabulary size. We illustrate that: this task is not solvable by a language model alone, our model combining 2D and 3D visual information indeed provides the best result, all models perform significantly worse than human-level. We provide human evaluation for responses given by different models and find that accuracy on the MovieFIB evaluation corresponds well with human judgment. We suggest avenues for improving video models, and hope that the MovieFIB challenge can be useful for measuring and encouraging progress in this very interesting field.

2017-07-21

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (published)

GuessWhat?! Visual Object Discovery through Multi-modal Dialogue

Harm de Vries

Florian Strub

Sarath Chandar

Olivier Pietquin

Hugo Larochelle

We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The… (see more) goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.

2017-07-21

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (published)

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

M. Cardoso

Tal Arbel

G. Carneiro

T. Syeda-Mahmood

J. Tavares

Mehdi Moradi

Andrew P. Bradley

Hayit Greenspan

J. Papa

Anant. Madabhushi

Jacinto C Nascimento

Jaime S. Cardoso

Vasileios Belagiannis

Zhi Lu

Faculdade Engenharia

2017-07-19

ArXiv (preprint)

A Closer Look at Memorization in Deep Networks

Devansh Arpit

Stanisław Jastrzębski

Nicolas Ballas

Maxinder S. Kanwal

Asja Fischer

We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While dee… (see more)p networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real data. Our analysis suggests that the notions of effective capacity which are dataset independent are unlikely to explain the generalization performance of deep networks when trained with gradient based methods because training data itself plays an important role in determining the degree of memorization.

2017-07-17

Proceedings of the 34th International Conference on Machine Learning (published)

proceedings.mlr.press

Accelerated Stochastic Power Iteration

Peng Xu

Bryan Dawei He

Christopher De Sa

Ioannis Mitliagkas

Christopher Re

Principal component analysis (PCA) is one of the most powerful tools in machine learning. The simplest method for PCA, the power iteration, … (see more)requires O ( 1 / Δ ) full-data passes to recover the principal component of a matrix with eigen-gap Δ. Lanczos, a significantly more complex method, achieves an accelerated rate of O ( 1 / Δ ) passes. Modern applications, however, motivate methods that only ingest a subset of available data, known as the stochastic setting. In the online stochastic setting, simple algorithms like Oja's iteration achieve the optimal sample complexity O ( σ 2 / Δ 2 ) . Unfortunately, they are fully sequential, and also require O ( σ 2 / Δ 2 ) iterations, far from the O ( 1 / Δ ) rate of Lanczos. We propose a simple variant of the power iteration with an added momentum term, that achieves both the optimal sample and iteration complexity. In the full-pass setting, standard analysis shows that momentum achieves the accelerated rate, O ( 1 / Δ ) . We demonstrate empirically that naively applying momentum to a stochastic method, does not result in acceleration. We perform a novel, tight variance analysis that reveals the "breaking-point variance" beyond which this acceleration does not occur. By combining this insight with modern variance reduction techniques, we construct stochastic PCA algorithms, for the online and offline setting, that achieve an accelerated iteration complexity O ( 1 / Δ ) . Due to the embarassingly parallel nature of our methods, this acceleration translates directly to wall-clock time if deployed in a parallel environment. Our approach is very general, and applies to many non-convex optimization problems that can now be accelerated using the same technique.

2017-07-10

ArXiv (preprint)

Learning Visual Reasoning Without Strong Priors

Ethan Perez

Harm de Vries

Florian Strub

Vincent Dumoulin

Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an… (see more) important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.

2017-07-10

ArXiv (preprint)

Detecting Large Concept Extensions for Conceptual Analysis

L. Chartrand

Jackie Cheung

Mohamed Bouguessa

2017-07-02

Machine Learning and Data Mining in Pattern Recognition (published)

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

Sean Lawlor

Michael Rabbat

Time-varying mixture models are useful for representing complex, dynamic distributions. Components in the mixture model can appear and disap… (see more)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.

2017-06-15

IEEE Transactions on Signal Processing (published)

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

Sean F. Lawlor

Michael Rabbat

Time-varying mixture models are useful for representing complex, dynamic distributions. Components in the mixture model can appear and disap… (see more)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.

2017-06-15

IEEE Transactions on Signal Processing (published)

Implementation of Sparse Superposition Codes

Carlo Condo

Warren Gross

Sparse superposition codes (SSCs) are capacity achieving codes whose decoding process is a linear sensing problem. Decoding approaches thus … (see more)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 (

2017-05-01

IEEE Transactions on Signal Processing (published)