Publications

Training End-to-End Analog Neural Networks with Equilibrium Propagation
Jack D. Kendall
Ross D. Pantone
Kalpana Manickavasagam
Yoshua Bengio
Benjamin Scellier
We introduce a principled method to train end-to-end analog neural networks by stochastic gradient descent. In these analog neural networks,… (voir plus) the weights to be adjusted are implemented by the conductances of programmable resistive devices such as memristors [Chua, 1971], and the nonlinear transfer functions (or `activation functions') are implemented by nonlinear components such as diodes. We show mathematically that a class of analog neural networks (called nonlinear resistive networks) are energy-based models: they possess an energy function as a consequence of Kirchhoff's laws governing electrical circuits. This property enables us to train them using the Equilibrium Propagation framework [Scellier and Bengio, 2017]. Our update rule for each conductance, which is local and relies solely on the voltage drop across the corresponding resistor, is shown to compute the gradient of the loss function. Our numerical simulations, which use the SPICE-based Spectre simulation framework to simulate the dynamics of electrical circuits, demonstrate training on the MNIST classification task, performing comparably or better than equivalent-size software-based neural networks. Our work can guide the development of a new generation of ultra-fast, compact and low-power neural networks supporting on-chip learning.
2020-06-01
ArXiv (prépublication)
arxiv.org
arxiv.org
Cross-layer communication over fading channels with adaptive decision feedback
Borna Sayedana
Aditya Mahajan
E. Yeh
In this paper, cross-layer design of transmitting data packets over AWGN fading channel with adaptive decision feedback is considered. The t… (voir plus)ransmitter decides the number of packets to transmit and the threshold of the decision feedback based on the queue length and the channel state. The transmit power is chosen such that the probability of error is below a pre-specified threshold. We model the system as a Markov decision process and use ideas from lattice theory to establish qualitative properties of optimal transmission strategies. In particular, we show that: (i) if the channel state remains the same and the number of packets in the queue increase, then the optimal policy either transmits more packets or uses a smaller decision feedback threshold or both; and (ii) if the number of packets in the queue remain the same and the channel quality deteriorates, then the optimal policy either transmits fewer packets or uses a larger threshold for the decision feedback or both. We also show under rate constraints that if the channel gains for all channel states are above a threshold, then the “or” in the above characterization can be replaced by “and”. Finally, we present a numerical example showing that adaptive decision feedback significantly improves the power-delay trade-off as compared with the case of no feedback.
2020-05-31
International Symposium on Modeling and Optimization in Mobile, Ad-Hoc and Wireless Networks (publié)
dblp.uni-trier.de
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks
Md Rifat Arefin
Vincent Michalski
Pierre-Luc St-Charles
Alfredo Kalaitzis
Sookyung Kim
Samira E. Kahou
Yoshua Bengio
High-resolution satellite imagery is critical for various earth observation applications related to environment monitoring, geoscience, fore… (voir plus)casting, and land use analysis. However, the acquisition cost of such high-quality imagery due to the scarcity of providers and needs for high-frequency revisits restricts its accessibility in many fields. In this work, we present a data-driven, multi-image super resolution approach to alleviate these problems. Our approach is based on an end-to-end deep neural network that consists of an encoder, a fusion module, and a decoder. The encoder extracts co-registered highly efficient feature representations from low-resolution images of a scene. A Gated Re-current Unit (GRU)-based module acts as the fusion module, aggregating features into a combined representation. Finally, a decoder reconstructs the super-resolved image. The proposed model is evaluated on the PROBA-V dataset released in a recent competition held by the European Space Agency. Our results show that it performs among the top contenders and offers a new practical solution for real-world applications.
2020-05-31
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)
doi.org
Population variability in social brain morphology for social support, household size and friendship satisfaction
Arezoo Taebi
Hannah Kiesow
Kai Vogeley
Leonhard Schilbach
Boris C. Bernhardt
Danilo Bzdok
The social brain hypothesis proposes that the complexity of human brains has coevolved with increasing complexity of social interactions in … (voir plus)primate societies. The present study explored the possible relationships between brain morphology and the richness of more intimate ‘inner’ and wider ‘outer’ social circles by integrating Bayesian hierarchical modeling with a large cohort sample from the UK Biobank resource (n = 10 000). In this way, we examined population volume effects in 36 regions of the ‘social brain’, ranging from lower sensory to higher associative cortices. We observed strong volume effects in the visual sensory network for the group of individuals with satisfying friendships. Further, the limbic network displayed several brain regions with substantial volume variations in individuals with a lack of social support. Our population neuroscience approach thus showed that distinct networks of the social brain show different patterns of volume variations linked to the examined social indices.
2020-05-31
Social Cognitive and Affective Neuroscience (publié)
doi.org
Restless bandits: indexability and computation of Whittle index
Nima Akbarzadeh
Aditya Mahajan
Restless bandits are a class of sequential resource allocation problems concerned with allocating one or more resources among several altern… (voir plus)ative processes where the evolution of the process depends on the resource allocated to them. Such models capture the fundamental trade-offs between exploration and exploitation. In 1988, Whittle developed an index heuristic for restless bandit problems which has emerged as a popular solution approach due to its simplicity and strong empirical performance. The Whittle index heuristic is applicable if the model satisfies a technical condition known as indexability. In this paper, we present two general sufficient conditions for indexability and identify simpler to verify refinements of these conditions. We then present a general algorithm to compute Whittle index for indexable restless bandits. Finally, we present a detailed numerical study which affirms the strong performance of the Whittle index heuristic.
2020-05-31
arXiv.org (prépublication)
dblp.uni-trier.de
GIANT: Scalable Creation of a Web-scale Ontology
Bang Liu
Weidong Guo
Di Niu
Jinwen Luo
Chaoyue Wang
Zhen Wen
Yu Xu
2020-05-30
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (publié)
doi.org
arxiv.org
Current works and future directions on application of machine learning in primary care
S. A. Rahimi
Vera Granikov
Pierre Pluye
In this short paper, we explained current machine learning works in primary care based on a scoping review that we performed. The performed … (voir plus)review was in line with the methodological framework proposed by Colquhoun and colleagues. Lastly, we discussed our observations and gave important directions to the future studies in this fast-growing area.
2020-05-26
International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (publié)
doi.org
Minimisation in Logical Form
Nick Bezhanishvili
Marcello M. Bonsangue
Helle Hvid Hansen
Dexter Kozen
C. Kupke
Prakash Panangaden
Alexandra Silva
2020-05-22
ArXiv (prépublication)
arxiv.org
arxiv.org
CNN to detect differences in cerebral cortical anatomy of left- and right- handers
Lisa Meyer-Baese
Erik Roecher
Lucas Moesch
Danilo Bzdok
Klaus Mathiak
Handedness is one of the most obvious functional asymmetries, but its relation to anatomical asymmetry in the brain has not yet been clearly… (voir plus) demonstrated. However, there is no significant evidence to prove or disprove this structure-function correlation, thus left-handed patients are often excluded from magnetic resonance imaging (MRI) studies. MRI classification of left and right hemispheres is a difficult task on its own due to the complexity of the images and the structural similarities between the two halves. We demonstrate a deep artificial neural network approach in connection with a detailed preprocessing pipeline for the classification of lateralization in T1-weighted MR images of the human brain. Preprocessing includes bias field correction and registration on the MNI template. Our classifier is a convolutional neural network (CNN) that was trained on 287 images. Each image was duplicated and mirrored on the mid-sagittal plane. The best model reached an accuracy of 97.594% with a mean of 95.42% and standard deviation of 1.37%. Additionally, our model’s performance was evaluated on an independent set of 118 images and reached a classification accuracy of 97%. In a larger study we tested the model on grey-matter images of 927 left and 927 right-handed patients from the UK Biobank. Here all right-handed images and all left-handed images were classified as belonging to one class. The results suggest that there is no structural difference in grey-matter between the two hemispheres that can be distinguished by the deep learning classifier.
2020-05-19
Defense + Commercial Sensing (publié)
doi.org
Failure to follow medication changes made at hospital discharge is associated with adverse events in 30 days
Daniala L. Weir
Aude Motulsky
Michal Abrahamowicz
Todd C. Lee
Steven Morgan
David L. Buckeridge
Robyn Tamblyn
To evaluate the hypothesis that nonadherence to medication changes made at hospital discharge is associated with an increased risk of advers… (voir plus)e events in the 30 days postdischarge. Patients admitted to hospitals in Montreal, Quebec, between 2014 and 2016. Prospective cohort study. Nonadherence to medication changes was measured by comparing medications dispensed in the community with those prescribed at hospital discharge. Patient, health system, and drug regimen‐level covariates were measured using medical services and pharmacy claims data as well as data abstracted from the patient's hospital chart. Multivariable Cox models were used to determine the association between nonadherence to medication changes and the risk of adverse events. Among 2655 patients who met our inclusion criteria, mean age was 69.5 years (SD 14.7) and 1581 (60%) were males. Almost half of patients (n = 1161, 44%) were nonadherent to at least one medication change, and 860 (32%) were readmitted to hospital, visited the emergency department, or died in the 30 days postdischarge. Patients who were not adherent to any of their medication changes had a 35% higher risk of adverse events compared to those who were adherent to all medication changes (1.41 vs 1.27 events/100 person‐days, adjusted hazard ratio: 1.35, 95% CI: 1.06‐1.71). Almost half of all patients were not adherent to some or all changes made to their medications at hospital discharge. Nonadherence to all changes was associated with an increased risk of adverse events. Interventions addressing barriers to adherence should be considered moving forward.
2020-05-19
Health Services Research (publié)
doi.org
Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis
Aaron Carass
Snehashis Roy
Adrian Gherman
Jacob C. Reinhold
Andrew Jesson
Tal Arbel
Oskar Maier
Heinz Handels
Mohsen Ghafoorian
Bram Platel
Ariel Birenbaum
Hayit Greenspan
Dzung L. Pham
Ciprian M. Crainiceanu
Peter A. Calabresi
Jerry L. Prince
William R. Gray Roncal
Russell T. Shinohara
Ipek Oguz
2020-05-18
Scientific Reports (publié)
doi.org
Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI
Joshua D. Durso-Finley
Douglas Arnold
Tal Arbel
2020-05-18
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (publié)
doi.org