Publications

Pix2Shape – Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation
Sai Rajeswar
Fahim Mannan
Florian Golemo
Jérôme Parent-lévesque
David Vázquez
Derek Nowrouzezahrai
Aaron Courville
We infer and generate three-dimensional (3D) scene information from a single input image and without supervision. This problem is under-expl… (see more)ored, with most prior work relying on supervision from, e.g., 3D ground-truth, multiple images of a scene, image silhouettes or key-points. We propose Pix2Shape, an approach to solve this problem with four components: (i) an encoder that infers the latent 3D representation from an image, (ii) a decoder that generates an explicit 2.5D surfel-based reconstruction of a scene from the latent code (iii) a differentiable renderer that synthesizes a 2D image from the surfel representation, and (iv) a critic network trained to discriminate between images generated by the decoder-renderer and those from a training distribution. Pix2Shape can generate complex 3D scenes that scale with the view-dependent on-screen resolution, unlike representations that capture world-space resolution, i.e., voxels or meshes. We show that Pix2Shape learns a consistent scene representation in its encoded latent space and that the decoder can then be applied to this latent representation in order to synthesize the scene from a novel viewpoint. We evaluate Pix2Shape with experiments on the ShapeNet dataset as well as on a novel benchmark we developed, called 3D-IQTT, to evaluate models based on their ability to enable 3d spatial reasoning. Qualitative and quantitative evaluation demonstrate Pix2Shape's ability to solve scene reconstruction, generation, and understanding tasks.
2020-03-19
International Journal of Computer Vision (unknown)
doi.org
arxiv.org
Multinational Investigation of Fracture Risk with Antidepressant Use by Class, Drug, and Indication
Robyn Tamblyn
David W. Bates
David L. Buckeridge
William G. Dixon
Nadyne Girard
Jennifer S. Haas
Bettina Habib
Usman Iqbal
Jack Li
Therese Sheppard
Antidepressants increase the risk of falls and fracture in older adults. However, risk estimates vary considerably even in comparable popula… (see more)tions, limiting the usefulness of current evidence for clinical decision making. Our aim was to apply a common protocol to cohorts of older antidepressant users in multiple jurisdictions to estimate fracture risk associated with different antidepressant classes, drugs, doses, and potential treatment indications. Retrospective (2009–2014) cohort study. Five jurisdictions in the United States, Canada, United Kingdom, and Taiwan. Older antidepressant users—subjects were followed from first antidepressant prescription or dispensation to first fracture or until the end of follow‐up. The risk of fractures with antidepressants was estimated by multivariable Cox proportional hazards models using time‐varying measures of antidepressant dose and use vs nonuse, adjusting for patient characteristics. Between 42.9% and 55.6% of study cohorts were 75 years and older, and 29.3% to 45.4% were men. Selective serotonin reuptake inhibitors (SSRIs) (48.4%‐60.0%) were the predominant class used in North America compared with tricyclic antidepressants (TCAs) in the United Kingdom and Taiwan (49.6%‐53.6%). Fracture rates varied from 37.67 to 107.18 per 1,000. The SSRIs citalopram (hazard ratio [HR] = 1.23; 95% confidence interval [CI] = 1.11‐1.36 to HR = 1.43; 95% CI = 1.11‐1.84) and sertraline (HR = 1.36; 95% CI = 1.10‐1.68), the SNRI duloxetine (HR = 1.41; 95% CI = 1.06‐1.88), TCAs doxepin (HR = 1.36; 95% CI = 1.00‐1.86) and imipramine (HR = 1.16; 95% CI = 1.05‐1.28), and atypicals (HR = 1.34; 95% CI = 1.14‐1.58) increased fracture risk in some but not all jurisdictions. In the United States and the United Kingdom, fracture risk with all classes was higher when prescribed for depression than chronic pain, a trend that is likely explained by drug choice. The fracture risk for patients may be reduced by selecting paroxetine, an SSRI with lower risk than citalopram, the SNRI venlafaxine over duloxetine, and the TCA amitriptyline over imipramine or doxepin. There is uncertainty about the risk associated with the atypical antidepressants. J Am Geriatr Soc 68:1494‐1503, 2020.
2020-03-16
Journal of the American Geriatrics Society (published)
doi.org
Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records
Yu Luo
David A. Stephens
Aman Verma
David L. Buckeridge
Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations… (see more). However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous‐time process potentially as a function of time‐varying covariates. We develop a continuous‐time hidden Markov model to analyze longitudinal data accounting for irregular visits and different types of observations. By employing a specific missing data likelihood formulation, we can construct an efficient computational algorithm. We focus on Bayesian inference for the model: this is facilitated by an expectation‐maximization algorithm and Markov chain Monte Carlo methods. Simulation studies demonstrate that these approaches can be implemented efficiently for large data sets in a fully Bayesian setting. We apply this model to a real cohort where patients suffer from chronic obstructive pulmonary disease with the outcome being the number of drugs taken, using health care utilization indicators and patient characteristics as covariates.
2020-03-10
Biometrics (published)
doi.org
Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration
Dominique Beaini
Sofiane Wozniak Achiche
Maxime Raison
2020-03-10
ArXiv (preprint)
arxiv.org
arxiv.org
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
Qicheng Lao
Xiang Jiang
Mohammad Havaei
Yoshua Bengio
Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task … (see more)drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the context of continuous domain adaptation, where the model is required to learn new tasks adapted to new domains in a non-stationary environment while maintaining previously learned knowledge. To deal with both drifts, we propose variational domain-agnostic feature replay, an approach that is composed of three components: an inference module that filters the input data into domain-agnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We address the two fundamental scenarios in continuous domain adaptation, demonstrating the effectiveness of our proposed approach for practical usage.
2020-03-08
ArXiv (preprint)
arxiv.org
arxiv.org
Beyond Backprop: Different Approaches to Credit Assignment in Neural Nets
Irina Rish
2020-03-04
(published)
www.semanticscholar.org
Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach
Julian Tillmann
M. Uljarevic
Daisy Crawley
G. Dumas
Eva Loth
D. Murphy
J. Buitelaar
Tony Charman
Jumana Sara Bonnie Sarah Christian Thomas Carsten Michael Daniel Claudia Yvette Bhismadev Ineke Flavio Dell’ Guillaume Christine Jessica Vincent Pilar David Hannah Joerg Mark H. Emily J. H. Prantik Meng-Chuan Xavier Liogier Michael David J. René Luke Andreas Carolin Nico Laurence Marianne Bob Gahan Antonio M. Barbara Amber Jessica Roberto Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Jumana Sara Bonnie Sarah Christian Thomas Carsten Michael Ahmad Ambrosino Auyeung Baumeister Beckmann Bourge
Jumana Ahmad
Sara Ambrosino
Bonnie Auyeung
Sarah Baumeister
Christian Beckmann
Thomas Bourgeron
Carsten Bours
Michael Brammer
Daniel Brandeis
Claudia Brogna … (see 39 more)
Yvette de Bruijn
Bhismadev Chakrabarti
Ineke Cornelissen
Flavio Dell’Acqua
Guillaume Dumas
Christine Ecker
Jessica Faulkner
Vincent Frouin
Pilar Garcés
David Goyard
Hannah Hayward
Joerg F. Hipp
Mark Johnson
Emily J. H. Jones
Prantik Kundu
Meng-Chuan Lai
Xavier Liogier D’ardhuy
Michael V. Lombardo
David J. Lythgoe
René Mandl
Luke Mason
Andreas Meyer-Lindenberg
Carolin Moessnang
Nico Mueller
Larry O’Dwyer
Marianne Oldehinkel
Bob Oranje
Gahan Pandina
Antonio Persico
Barbara Ruggeri
Amber N. V. Ruigrok
Jessica Sabet
Roberto Sacco
Roberto Toro
Heike Tost
Jack Waldman
Steve C. R. Williams
Caroline Wooldridge
Marcel P. Zwiers
2020-03-04
Molecular Autism (published)
doi.org
Not one model fits all: unfairness in RSFC-based prediction of behavioral data in African American
Jingwei Li
Danilo Bzdok
Avram J. Holmes
Thomas B.t. Yeo
Sarah Genon
14 Helmholtz AI kick-off meeting 5 Mar 2020, 14:17:33 Page 1/1 Abstract #14 | Poster Not one model fits all: unfairness in RSFC-based predic… (see more)tion of behavioral data in African American J. Li , D. Bzdok, A. Holmes, T. Yeo, S. Genon 1 Forschungszentrum Julich, Institute of Neuroscience and Medicine, Jülich, Germany 2 McGill University, Department of Biomedical Imaging, Montreal, Canada 3 National University of Singapore, ECE, CSC, CIRC, N.1 & MNP, Singapore, Singapore 4 Yale University, New Haven, United States of America While predictive models are expected to play a major role in personalized medicine approaches in the future, biases towards specific population groups have been evidenced, hence raising concerns about the risks of unfairness of machine learning algorithms. As great hopes and intense work have been invested recently in the prediction of behavioral phenotypes based on brain resting-state functional connectivity (RSFC), we here examined potential differences in RSFC-based predictive models of behavioral data between African American (AA) and White American (WA) samples matched for the main demographic, anthropometric, behavioral and in-scanner motion variables. We used resting-fMRI data with 58 behavioral measures of 953 subjects comprising 130 African American (AA) and 724 White American (WA). For each subject, a 419 x 419 matrix summarizing connectivity of 419 brain regions was computed. Matching between AA and WA was performed at the subject level by creating 102 pairs of AA and WA subjects, matched for 6 types of variables (age, sex, intracranial volume, education, in-scanner motion and behavioral scores). We performed 10-fold nested cross-validation by randomly splitting the 102 pairs across 10 sets. The remaining 749 subjects were also divided across the 10 sets. A predictive model was built for each behavioral variable by using kernel ridge regression. All analyses focused on the 102 matched AA and WA groups. After FDR correction (q 0.05), no significant difference was found between the matched AA and WA groups for the matching variables. Out of 58 behavioral variables, 38 showed significantly above chance prediction accuracies (based on permutation test, FDR corrected). Overall, average prediction performance for these variables was higher in the WA group than in the AA group. Furthermore, significant differences in prediction performance between the two groups were found in 35 behavioral variables (FDR corrected; q 0.05). Our results suggest that RSFC-based prediction models of behavioral phenotype trained on the entire HCP population show different prediction performance in different subsets of the population. This suggest that one model might not fit all that, in some cases, RSFC-based predictive models might have poorer prediction accuracies for African Americans compared to matched White Americans. Future work should evaluate the factors contributing to these discrepancies and the potential consequences, as well as possible recommendations.
2020-03-04
(published)
www.semanticscholar.org
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Stefano Alletto
Shenyang Huang
Vincent François-Lavet
Yohei Nakata
Guillaume Rabusseau
Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it fin… (see more)ds. Should it be the only metric for a good autoML approach? To examine aspects beyond performance, we propose a set of criteria aimed at evaluating the core of autoML problem: the amount of human intervention required to deploy these methods into real world scenarios. Based on our proposed evaluation checklist, we study the effectiveness of a random search strategy for fully automated multimodal neural architecture search. Compared to traditional methods that rely on manually crafted feature extractors, our method selects each modality from a large search space with minimal human supervision. We show that our proposed random search strategy performs close to the state of the art on the AV-MNIST dataset while meeting the desirable characteristics for a fully automated design process.
2020-03-01
ArXiv (preprint)
arxiv.org
arxiv.org
10,000 social brains: Sex differentiation in human brain anatomy
Hannah Kiesow
Robin I. M. Dunbar
Joseph W. Kable
Tobias Kalenscher
Kai Vogeley
Leonhard Schilbach
Andre F. Marquand
Thomas V. Wiecki
Danilo Bzdok
Population variability in social lifestyle is reflected in brain morphology in sex-dependent ways.
2020-02-29
Science Advances (published)
doi.org
Multiple Kernel Learning-Based Transfer Regression for Electric Load Forecasting
Di Wu
Boyu Wang
Doina Precup
Benoit Boulet
Electric load forecasting, especially short-term load forecasting (STLF), is becoming more and more important for power system operation. We… (see more) propose to use multiple kernel learning (MKL) for residential electric load forecasting which provides more flexibility than traditional kernel methods. Computation time is an important issue for short-term forecasting, especially for energy scheduling. However, conventional MKL methods usually lead to complicated optimization problems. Another practical issue for this application is that there may be a very limited amount of data available to train a reliable forecasting model for a new house, while at the same time we may have historical data collected from other houses which can be leveraged to improve the prediction performance for the new house. In this paper, we propose a boosting-based framework for MKL regression to deal with the aforementioned issues for STLF. In particular, we first adopt boosting to learn an ensemble of multiple kernel regressors and then extend this framework to the context of transfer learning. Furthermore, we consider two different settings: homogeneous transfer learning and heterogeneous transfer learning. Experimental results on residential data sets demonstrate that forecasting error can be reduced by a large margin with the knowledge learned from other houses.
2020-02-29
IEEE Transactions on Smart Grid (published)
doi.org
Seven pillars of precision digital health and medicine
Arash Shaban-Nejad
Martin Michalowski
Niels Peek
John S. Brownstein
David L Buckeridge
2020-02-29
Artificial Intelligence in Medicine (published)
doi.org