Publications

Empowering Clinicians with MeDT: A Framework for Sepsis Treatment
Aamer Abdul Rahman
Pranav Agarwal
Vincent Michalski
Rita Noumeir
Philippe Jouvet
Samira Ebrahimi Kahou
2019-12-31
International Conference on Machine Learning (publié)
openreview.net
openreview.net
AN ENSEMBLE APPROACH FOR DETECTING MACHINE FAILURE FROM SOUND Technical
Faruk Ahmed
Phong Cao Nguyen
Aaron Courville
We develop an ensemble-based approach for our submission to the anomaly detection challenge at DCASE 2020. The main members of our ensemble … (voir plus)are auto-encoders (with reconstruction error as the signal), classifiers (with negative predictive confidence as the signal), mismatch of the time-shifted signal with its Fourier-phase-shifted version, and a Gaussian mixture model on a set of common short-term features extracted from the waveform. The scores are passed through an exponential non-linearity and weighted to provide the final score, where the weighting and scaling hyper-parameters are learned on the development set. Our ensemble improves over the baseline on the development set.
2019-12-31
(publié)
www.semanticscholar.org
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
Tanya Nair
Doina Precup
Douglas L. Arnold
Tal Arbel
Deep learning (DL) networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge dat… (voir plus)asets [3,11,16], especially for large pathologies. However, in the context of diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions visible on MRI sequences, even very small ones, is essential for disease staging, prognosis, and evaluating treatment efficacy. Moreover, producing deterministic outputs hinders DL adoption into clinical routines. Uncertainty estimates for the predictions would permit subsequent revision by clinicians. We present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout [4] in the context of deep networks for lesion detection and segmentation in medical images. Specifically, we develop a 3D MS lesion segmentation CNN, augmented to provide four different voxel-based uncertainty measures based on MC dropout. We train the network on a proprietary, large-scale, multi-site, multi-scanner, clinical MS dataset, and compute lesion-wise uncertainties by accumulating evidence from voxel-wise uncertainties within detected lesions. We analyze the performance of voxel-based segmentation and lesion-level detection by choosing operating points based on the uncertainty. Empirical evidence suggests that uncertainty measures consistently allow us to choose superior operating points compared only using the network's sigmoid output as a probability.
2019-12-31
Medical Image Analysis (publié)
doi.org
arxiv.org
Fairness in Kidney Exchange Programs through Optimal Solutions Enumeration
Golnoosh Farnadi
Behrouz Babaki
Margarida Carvalho
Not all patients who need kidney transplant can find a donor with compatible characteristics. Kidney exchange programs (KEPs) seek to match … (voir plus)such incompatible patient-donor pairs together, usually with the objective of maximizing the total number of transplants. We propose a randomized policy for selecting an optimal solution in which patients’ equity of opportunity to receive a transplant is promoted. Our approach gives rise to the problem of enumerating all optimal solutions, which we tackle using a hybrid of constraint programming and linear programming. We empirically demonstrate the advantages of our proposed method over the common practice of using the first optimal solution obtained by a solver.
2019-12-31
(publié)
www.semanticscholar.org
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents
Christian Rupprecht
Cyril Ibrahim
Christopher Pal
As deep reinforcement learning driven by visual perception becomes more widely used there is a growing need to better understand and probe t… (voir plus)he learned agents. Understanding the decision making process and its relationship to visual inputs can be very valuable to identify problems in learned behavior. However, this topic has been relatively under-explored in the research community. In this work we present a method for synthesizing visual inputs of interest for a trained agent. Such inputs or states could be situations in which specific actions are necessary. Further, critical states in which a very high or a very low reward can be achieved are often interesting to understand the situational awareness of the system as they can correspond to risky states. To this end, we learn a generative model over the state space of the environment and use its latent space to optimize a target function for the state of interest. In our experiments we show that this method can generate insights for a variety of environments and reinforcement learning methods. We explore results in the standard Atari benchmark games as well as in an autonomous driving simulator. Based on the efficiency with which we have been able to identify behavioural weaknesses with this technique, we believe this general approach could serve as an important tool for AI safety applications.
2019-12-31
ICLR.cc/2020/Conference (poster)
openreview.net
openreview.net
Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists
Hao-Ting Wang
Jonathan Smallwood
Janaina Mourao-Miranda
Cedric Huchuan Xia
Theodore D. Satterthwaite
Danielle S. Bassett
Danilo Bzdok
2019-12-31
NeuroImage (publié)
doi.org
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
Boris Knyazev
Harm de Vries
Cătălina Cangea
Graham W. Taylor
Aaron Courville
Eugene Belilovsky
Scene graph generation (SGG) aims to predict graph-structured descriptions of input images, in the form of objects and relationships between… (voir plus) them. This task is becoming increasingly useful for progress at the interface of vision and language. Here, it is important - yet challenging - to perform well on novel (zero-shot) or rare (few-shot) compositions of objects and relationships. In this paper, we identify two key issues that limit such generalization. Firstly, we show that the standard loss used in this task is unintentionally a function of scene graph density. This leads to the neglect of individual edges in large sparse graphs during training, even though these contain diverse few-shot examples that are important for generalization. Secondly, the frequency of relationships can create a strong bias in this task, such that a blind model predicting the most frequent relationship achieves good performance. Consequently, some state-of-the-art models exploit this bias to improve results. We show that such models can suffer the most in their ability to generalize to rare compositions, evaluating two different models on the Visual Genome dataset and its more recent, improved version, GQA. To address these issues, we introduce a density-normalized edge loss, which provides more than a two-fold improvement in certain generalization metrics. Compared to other works in this direction, our enhancements require only a few lines of code and no added computational cost. We also highlight the difficulty of accurately evaluating models using existing metrics, especially on zero/few shots, and introduce a novel weighted metric.
2019-12-31
Proceedings of the British Machine Vision Conference 2020 (publié)
doi.org
arxiv.org
GraphMix: Improved Training of Graph Neural Networks for Semi-Supervised Learning
Vikas Verma
Meng Qu
Alex Lamb
Yoshua Bengio
Juho Kannala
Jian Tang
We present GraphMix , a regularized training scheme for Graph Neural Network based semi-supervised object classification, leveraging the re… (voir plus)cent advances in the regularization of classical deep neural networks. Specifically, we pro-pose a unified approach in which we train a fully-connected network jointly with the graph neural network via parameter sharing, interpolation-based regularization and self-predicted-targets. Our proposed method is architecture agnostic in the sense that it can be applied to any variant of graph neural networks which applies a parametric transformation to the features of the graph nodes. Despite its simplicity, with GraphMix we can consistently improve results and achieve or closely match state-of-the-art performance using even simpler architectures such as Graph Convolutional Networks, across three established graph benchmarks: Cora, Citeseer and Pubmed citation network datasets, as well as three newly proposed datasets :Cora-Full, Co-author-CS and Co-author-Physics.
2019-12-31
(publié)
www.semanticscholar.org
How to make your optimizer generalize better
Sharan Vaswani
Reza Babenzhad
Sait AI Lab
Montreal
Jose Gallego
Aaron Mishkin
Simon Lacoste-Julien
Nicolas Roux
We study the implicit regularization of optimization methods for linear models interpolating the training data in the under-parametrized and… (voir plus) over-parametrized regimes. For over-parameterized linear regression, where there are infinitely many interpolating solutions, different optimization methods can converge to solutions with varying generalization performance. In this setting, we show that projections onto linear spans can be used to move between solutions. Furthermore, via a simple reparameterization, we can ensure that an arbitrary optimizer converges to the minimum (cid:96) 2 -norm solution with favourable generalization properties. For under-parameterized linear clas-sification, optimizers can converge to different decision boundaries separating the data. We prove that for any such classifier, there exists a family of quadratic norms (cid:107)·(cid:107) P such that the classifier’s direction is the same as that of the maximum P -margin solution. We argue that analyzing convergence to the standard maximum (cid:96) 2 -margin is arbitrary and show that minimizing the norm induced by the data can result in better generalization. We validate our theoretical results via experiments on synthetic and real datasets.
2019-12-31
(publié)
www.semanticscholar.org
Investigating the Influence of Selected Linguistic Features on Authorship Attribution using German News Articles
Manuel Sage
Pietro Cruciata
Raed Abdo
Jackie CK Cheung
Yaoyao Fiona Zhao
In this work, we perform authorship attri-bution on a new dataset of German news articles. We seek to classify over 3,700 articles to their … (voir plus)five corresponding authors, using four conventional machine learning approaches (na¨ıve Bayes, logistic regression, SVM and kNN) and a convolutional neural network. We analyze the effect of character and word n-grams on the prediction accuracy, as well as the influence of stop words, punctuation, numbers, and lowercasing when preprocessing raw text. The experiments show that higher order character n-grams (n = 5,6) perform better than lower orders and word n-grams slightly outperform those with characters. Combining both in fusion models further improves results up to 92% for SVM. A multilayer convolutional structure allows the CNN to achieve 90.5% accuracy. We found stop words and punctuation to be important features for author identification; removing them leads to a measurable decrease in performance. Finally, we evaluate the topic dependency of the algorithms by gradually replacing named entities, nouns, verbs and eventually all to-kens in the dataset according to their POS-tags.
2019-12-31
SwissText/KONVENS (publié)
dblp.uni-trier.de
Investigating the interconnections between human, technology and context in the implementation of a AI-based health information technology: a dynamic technological frame perspective
Catherine Régis
2019-12-31
(publié)
www.semanticscholar.org
Joint Learning of Generative Translator and Classifier for Visually Similar Classes
Byungin Yoo
Tristan Sylvain
Yoshua Bengio
Junmo Kim
In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings wh… (voir plus)ere classes are visually similar and data is scarce. For this purpose, we propose joint learning from a scratch to train a classifier and a generative stochastic translation network end-to-end. The translation network is used to perform on-line data augmentation across classes, whereas previous works have mostly involved domain adaptation. To help the model further benefit from this data-augmentation, we introduce an adaptive fade-in loss and a quadruplet loss. We perform experiments on multiple datasets to demonstrate the proposed method’s performance in varied settings. Of particular interest, training on 40% of the dataset is enough for our model to surpass the performance of baselines trained on the full dataset. When our architecture is trained on the full dataset, we achieve comparable performance with state-of-the-art methods despite using a light-weight architecture.
2019-12-31
IEEE Access (publié)
doi.org
arxiv.org