Publications

Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

Matt Grenander

Robert Belfer

Ekaterina Kochmar

Iulian V. Serban

Franccois St-Hilaire

We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect … (voir plus)concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.

2021-05-18

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

DIBS: Diversity inducing Information Bottleneck in Model Ensembles

Samarth Sinha

Homanga Bharadhwaj

Anirudh Goyal

Hugo Larochelle

Animesh Garg

Florian Shkurti

2021-05-18

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Individual Fairness in Kidney Exchange Programs

Golnoosh Farnadi

William St-Arnaud

Behrouz Babaki

Margarida Carvalho

2021-05-18

AAAI Conference on Artificial Intelligence (publié)

doi.org

Meta-learning framework with applications to zero-shot time-series forecasting

Boris Oreshkin

Dmitri Carpov

Nicolas Chapados

Yoshua Bengio

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (voir plus) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

2021-05-18

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Metrics and continuity in reinforcement learning

Charline Le Lan

Marc Gendron-Bellemare

Pablo Samuel Castro

2021-05-18

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Self-Supervised Attention-Aware Reinforcement Learning

Haiping Wu

Khimya Khetarpal

Doina Precup

Visual saliency has emerged as a major visualization tool for interpreting deep reinforcement learning (RL) agents. However, much of the exi… (voir plus)sting research uses it as an analyzing tool rather than an inductive bias for policy learning. In this work, we use visual attention as an inductive bias for RL agents. We propose a novel self-supervised attention learning approach which can 1. learn to select regions of interest without explicit annotations, and 2. act as a plug for existing deep RL methods to improve the learning performance. We empirically show that the self-supervised attention-aware deep RL methods outperform the baselines in the context of both the rate of convergence and performance. Furthermore, the proposed self-supervised attention is not tied with specific policies, nor restricted to a specific scene. We posit that the proposed approach is a general self-supervised attention module for multi-task learning and transfer learning, and empirically validate the generalization ability of the proposed method. Finally, we show that our method learns meaningful object keypoints highlighting improvements both qualitatively and quantitatively.

2021-05-18

AAAI Conference on Artificial Intelligence (publié)

doi.org

Variance Penalized On-Policy and Off-Policy Actor-Critic

Arushi Jain

Gandharv Patil

Ayush Jain

Khimya Khetarpal

Doina Precup

2021-05-18

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

doi.org

arxiv.org

Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry

Natasha Bertelsen

Isotta Landi

Richard A.I. Bethlehem

Jakob Seidlitz

Elena Maria Busuoli

Veronica Mandelli

Eleonora Satta

Stavros Trakoshis

Bonnie Auyeung

Prantik Kundu

Eva Loth

Guillaume Dumas

Sarah Baumeister

Christian Beckmann

Sven Bölte

Thomas Bourgeron

Tony Charman

Sarah Durston

Christine Ecker

Rosemary Holt … (voir 57 de plus)

Mark Johnson

Emily J. H. Jones

Luke Mason

Andreas Meyer-Lindenberg

Carolin Moessnang

Marianne Oldehinkel

Antonio Persico

Julian Tillmann

Steve C. R. Williams

Will Spooren

Declan Murphy

Jan K. Buitelaar

Jumana Sara Tobias Carsten Michael Daniel Claudia Yvette Bhismadev Chris Ineke Daisy Flavio Jessica Vincent Pilar David Lindsay Hannah Joerg Rosemary J. Xavier Liogier David J. René Andre Maarten Nico Bethany Laurence Bob Gahan Antonio M. Barbara Amber N. V. Jessica Roberto Antonia San José Emily Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad

Simon Baron-Cohen

Jumana Ahmad

Meng-Chuan Lai

Sara Ambrosino

Michael V. Lombardo

Tobias Banaschewski

Carsten Bours

Michael Brammer

Daniel Brandeis

Claudia Brogna

Yvette de Bruijn

Bhismadev Chakrabarti

Christopher H. Chatham

Ineke Cornelissen

Daisy Crawley

Flavio Dell’Acqua

Jessica Faulkner

Vincent Frouin

Pilar Garcés

David Goyard

Lindsay Ham

Hannah Hayward

Joerg F. Hipp

Xavier Liogier D’ardhuy

David J. Lythgoe

René Mandl

Andre Marquand

Maarten Mennes

Nico Mueller

Beth Oakley

Laurence O’Dwyer

Bob Oranje

Gahan Pandina

Barbara Ruggeri

Amber N. V. Ruigrok

Jessica Sabet

Roberto Sacco

Antonia San José Cáceres

Emily Simonoff

Roberto Toro

Heike Tost

Jack Waldman

Caroline Wooldridge

Marcel P. Zwiers

2021-05-14

Communications Biology (publié)

doi.org

Recurrent Traumatic Brain Injury Surveillance Using Administrative Health Data: A Bayesian Latent Class Analysis

Oliver Lasry

Nandini Dendukuri

Judith Marcoux

David Buckeridge

Background: The initial injury burden from incident TBI is significantly amplified by recurrent TBI (rTBI). Unfortunately, research assessin… (voir plus)g the accuracy to conduct rTBI surveillance is not available. Accurate surveillance information on recurrent injuries is needed to justify the allocation of resources to rTBI prevention and to conduct high quality epidemiological research on interventions that mitigate this injury burden. This study evaluates the accuracy of administrative health data (AHD) surveillance case definitions for rTBI and estimates the 1-year rTBI incidence adjusted for measurement error. Methods: A 25% random sample of AHD for Montreal residents from 2000 to 2014 was used in this study. Four widely used TBI surveillance case definitions, based on the International Classification of Disease and on radiological exams of the head, were applied to ascertain suspected rTBI cases. Bayesian latent class models were used to estimate the accuracy of each case definition and the 1-year rTBI measurement-error-adjusted incidence without relying on a gold standard rTBI definition that does not exist, across children (18 years), adults (18-64 years), and elderly (> =65 years). Results: The adjusted 1-year rTBI incidence was 4.48 (95% CrI 3.42, 6.20) per 100 person-years across all age groups, as opposed to a crude estimate of 8.03 (95% CrI 7.86, 8.21) per 100 person-years. Patients with higher severity index TBI had a significantly higher incidence of rTBI compared to patients with lower severity index TBI. The case definition that identified patients undergoing a radiological examination of the head in the context of any traumatic injury was the most sensitive across children [0.46 (95% CrI 0.33, 0.61)], adults [0.79 (95% CrI 0.64, 0.94)], and elderly [0.87 (95% CrI 0.78, 0.95)]. The most specific case definition was the discharge abstract database in children [0.99 (95% CrI 0.99, 1.00)], and emergency room visits claims in adults/elderly [0.99 (95% CrI 0.99, 0.99)]. Median time to rTBI was the shortest in adults (75 days) and the longest in children (120 days). Conclusion: Conducting accurate surveillance and valid epidemiological research for rTBI using AHD is feasible when measurement error is accounted for.

2021-05-14

Frontiers in Neurology (publié)

doi.org

Common limitations of performance metrics in biomedical image analysis

Annika Reinke

Matthias Eisenmann

Minu Dietlinde Tizabi

Carole H. Sudre

TIM RÄDSCH

Michela Antonelli

Tal Arbel

Spyridon Bakas

M. Jorge Cardoso

Veronika Cheplygina

Keyvan Farahani

Ben Glocker

DOREEN HECKMANN-NÖTZEL

Fabian Isensee

Pierre Jannin

Charles Kahn

Jens Kleesiek

Tahsin Kurc

Michal Kozubek

Bennett Landman … (voir 15 de plus)

GEERT LITJENS

Klaus Maier-Hein

Anne Lousise Martel

Bjoern Menze

Henning Müller

Jens Petersen

Mauricio Reyes

Nicola Rieke

Bram Stieltjes

Ronald M. Summers

Sotirios A. Tsaftaris

Bram van Ginneken

Annette Kopp-Schneider

Paul Jäger

Lena Maier-Hein

2021-05-11

MIDL.io/2021/Conference/Short (poster)

openreview.net

Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting

Brennan Nichyporuk

Justin Szeto

Douglas Arnold

Tal Arbel

There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patie… (voir plus)nt images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images.

2021-05-11

MIDL.io/2021/Conference/Short (poster)

openreview.net