Publications

On Neural Architecture Inductive Biases for Relational Tasks
Giancarlo Kerg
Sarthak Mittal
David Rolnick
Yoshua Bengio
Blake Richards
Guillaume Lajoie
Current deep learning approaches have shown good in-distribution generalization performance, but struggle with out-of-distribution generaliz… (see more)ation. This is especially true in the case of tasks involving abstract relations like recognizing rules in sequences, as we find in many intelligence tests. Recent work has explored how forcing relational representations to remain distinct from sensory representations, as it seems to be the case in the brain, can help artificial systems. Building on this work, we further explore and formalize the advantages afforded by 'partitioned' representations of relations and sensory details, and how this inductive bias can help recompose learned relational structure in newly encountered settings. We introduce a simple architecture based on similarity scores which we name Compositional Relational Network (CoRelNet). Using this model, we investigate a series of inductive biases that ensure abstract relations are learned and represented distinctly from sensory data, and explore their effects on out-of-distribution generalization for a series of relational psychophysics tasks. We find that simple architectural choices can outperform existing models in out-of-distribution generalization. Together, these results show that partitioning relational representations from other information streams may be a simple way to augment existing network architectures' robustness when performing out-of-distribution relational computations.
2022-06-09
ArXiv (preprint)
doi.org
arxiv.org
Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems
Devang Kulshreshtha
Muhammad Shayan
Robert Belfer
Siva Reddy
Iulian V. Serban
Ekaterina Kochmar
2022-06-08
ArXiv (preprint)
doi.org
arxiv.org
Sequential Density Estimation via NCWFAs Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata
Tianyu Li
Bogdan Mazoure
Guillaume Rabusseau
Weighted finite automata (WFAs) have been widely applied in many fields. One of the classic problems for WFAs is probability distribution es… (see more)timation over sequences of discrete symbols. Although WFAs have been extended to deal with continuous input data, namely continuous WFAs (CWFAs), it is still unclear how to approximate density functions over sequences of continuous random variables using WFA-based models, due to the limitation on the expressiveness of the model as well as the tractability of approximating density functions via CWFAs. In this paper, we propose a nonlinear extension to the CWFA model to first improve its expressiveness, we refer to it as the nonlinear continuous WFAs (NCWFAs). Then we leverage the so-called RNADE method, which is a well-known density estimator based on neural networks, and propose the RNADE-NCWFA model. The RNADE-NCWFA model computes a density function by design. We show that this model is strictly more expressive than the Gaussian HMM model, which CWFA cannot approximate. Empirically, we conduct a synthetic experiment using Gaussian HMM generated data. We focus on evaluating the model's ability to estimate densities for sequences of varying lengths (longer length than the training data). We observe that our model performs the best among the compared baseline methods.
2022-06-08
ArXiv (preprint)
doi.org
arxiv.org
Interacting brains revisited: A cross‐brain network neuroscience perspective
Christian Gerloff
Kerstin Konrad
Danilo Bzdok
Christina Büsing
Vanessa Reindl
2022-06-06
Human Brain Mapping (published)
doi.org
Technologically-assisted communication attenuates inter-brain synchrony
Linoy Schwartz
Jonathan Levy
Yaara Endevelt-Shapira
Amir Djalovski
Olga Hayut
Guillaume Dumas
Ruth Pinkenson Feldman
2022-06-06
NeuroImage (published)
doi.org
How Can Digital Mental Health Enhance Psychiatry?
Emilie Stern
Jean-Arthur MICOULAUD FRANCHI
Guillaume Dumas
Jeverson Moreira
Stephane Mouchabac
Julia Maruani
Pierre Philip
Michel Lejoyeux
Pierre A. GEOFFROY
The use of digital technologies is constantly growing around the world. The wider-spread adoption of digital technologies and solutions in t… (see more)he daily clinical practice in psychiatry seems to be a question of when, not if. We propose a synthesis of the scientific literature on digital technologies in psychiatry and discuss the main aspects of its possible uses and interests in psychiatry according to three domains of influence that appeared to us: 1) assist and improve current care: digital psychiatry allows for more people to have access to care by simply being more accessible but also by being less stigmatized and more convenient; 2) develop new treatments: digital psychiatry allows for new treatments to be distributed via apps, and practical guidelines can reduce ethical challenges and increase the efficacy of digital tools; and 3) produce scientific and medical knowledge: digital technologies offer larger and more objective data collection, allowing for more detection and prevention of symptoms. Finally, ethical and efficacy issues remain, and some guidelines have been put forth on how to safely use these solutions and prepare for the future.
2022-06-04
The Neuroscientist (published)
doi.org
Modeling electronic health record data using a knowledge-graph-embedded topic model
Yuesong Zou
Ahmad Pesaranghader
Aman Verma
David Buckeridge
Yue Li
The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic wa… (see more)y. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present KG-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. KG-ETM distills latent disease topics from EHR data by learning the embedding from the medical knowledge graphs. We applied KG-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. KG-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations.
2022-06-03
ArXiv (preprint)
doi.org
arxiv.org
Genetic correlates of phenotypic heterogeneity in autism
Varun Warrier
Xinhe Zhang
Patrick Reed
Alexandra Havdahl
Tyler M. Moore
Freddy Cliquet
Claire Leblond
Thomas Rolland
Anders Rosengren
Antonia San Jose Hannah Daisy Jessica Jessica Claire Betha Caceres Hayward Crawley Faulkner Sabet Ellis Oakle
Antonia San Jose Hannah Daisy Jessica Jessica Claire Bethany Eva Tony Declan Rosemary Jack Jessica Nicola Meng-Chuan Gwilym Amber Emily Hisham Julia Sara Ambrosino Sarai Yvonne Tabitha Miriam Alyssia Iris Maarten Anna Ver Loren Nico Sarah Larry Carsten Annika Daniel Ineke Yvette Maartje Elzbieta Elodie Kristiina Rouslan Guillaume Yang-Min Thomas Caceres
Antonia San José Cáceres
Hannah Hayward
Daisy Crawley
Jessica Faulkner
Jessica Sabet
Claire Ellis
Beth Oakley
Eva Loth
Tony Charman … (see 67 more)
Declan Murphy
Rosemary Holt
Jack Waldman
Jessica Upadhyay
Nicola Gunby
Meng-Chuan Lai
Gwilym Renouf
Amber N. V. Ruigrok
Emily Taylor
Hisham Ziauddeen
Julia Deakin
Sara Ambrosino di Bruttopilo
Sarai van Dijk
Yvonne Rijks
Tabitha Koops
Miriam Douma
Alyssia Spaan
Iris Selten
Maarten Steffers
Anna Ver Loren van Themaat
Nico Bast
Sarah Baumeister
Larry O’Dwyer
Carsten Bours
Annika Rausch
Daniel von Rhein
Ineke Cornelissen
Yvette de Bruin
Maartje Graauwmans
Elzbieta Kostrzewa
Elodie Cauvet
Kristiina Tammimies
Rouslan Sitnikow
Guillaume Dumas
Yang-Min Kim
Thomas Bourgeron
David M. Jonas Thomas Preben Bo Ole Merete Hougaard
David M. Hougaard
Jonas Bybjerg-Grauholm
Thomas Werge
Preben Bo Mortensen
Ole Mors
Merete Nordentoft
Dwaipayan Armandina Carrie Isabelle Tracey Paula Alex Graham J. Alexander E. P. Lidia V. Tal Madeline A. Deepak P. Jonathan Adhya
Dwaipayan Armandina Carrie Isabelle Tracey Paula Alex Graham Adhya Alamanza Allison Garvey Parsons Smith Tsompa
Dwaipayan Adhya
Armandina Alamanza
Carrie Allison
Isabelle Garvey
Tracey Parsons
Paula Smith
Alex Tsompanidis
Graham J. Burton
Alexander E. P. Heazell
Lidia V. Gabis
Tal Biron-Shental
Madeline A. Lancaster
Deepak P. Srivastava
Jonathan Mill
David H. Rowitch
Matthew E. Hurles
Daniel H. Geschwind
Anders D. Børglum
Elise B. Robinson
Jakob Grove
Hilary C. Martin
Simon Baron-Cohen
2022-06-02
Nature Genetics (published)
doi.org
Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers
Miguel Saavedra-Ruiz
Sacha Morin
Liam Paull
In this work, we consider the problem of learning a perception model for monocular robot navigation using few annotated images. Using a Visi… (see more)on Transformer (ViT) pretrained with a label-free self-supervised method, we successfully train a coarse image segmentation model for the Duckietown environment using 70 training images. Our model performs coarse image segmentation at the
2022-06-02
2022 19th Conference on Robots and Vision (CRV) (published)
doi.org
arxiv.org
Ageism and Artificial Intelligence: Protocol for a Scoping Review
Charlene H Chu
Kathleen Leslie
Jiamin Shi
Rune Nyrup
Andria Bianchi
Shehroz S Khan
Samira Abbasgholizadeh-Rahimi
Alexandra Lyn
Amanda Grenier
Background Artificial intelligence (AI) has emerged as a major driver of technological development in the 21st century, yet little attention… (see more) has been paid to algorithmic biases toward older adults. Objective This paper documents the search strategy and process for a scoping review exploring how age-related bias is encoded or amplified in AI systems as well as the corresponding legal and ethical implications. Methods The scoping review follows a 6-stage methodology framework developed by Arksey and O’Malley. The search strategy has been established in 6 databases. We will investigate the legal implications of ageism in AI by searching grey literature databases, targeted websites, and popular search engines and using an iterative search strategy. Studies meet the inclusion criteria if they are in English, peer-reviewed, available electronically in full text, and meet one of the following two additional criteria: (1) include “bias” related to AI in any application (eg, facial recognition) and (2) discuss bias related to the concept of old age or ageism. At least two reviewers will independently conduct the title, abstract, and full-text screening. Search results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guideline. We will chart data on a structured form and conduct a thematic analysis to highlight the societal, legal, and ethical implications reported in the literature. Results The database searches resulted in 7595 records when the searches were piloted in November 2021. The scoping review will be completed by December 2022. Conclusions The findings will provide interdisciplinary insights into the extent of age-related bias in AI systems. The results will contribute foundational knowledge that can encourage multisectoral cooperation to ensure that AI is developed and deployed in a manner consistent with ethical values and human rights legislation as it relates to an older and aging population. We will publish the review findings in peer-reviewed journals and disseminate the key results with stakeholders via workshops and webinars. Trial Registration OSF Registries AMG5P; https://osf.io/amg5p International Registered Report Identifier (IRRID) DERR1-10.2196/33211
2022-06-01
JMIR Research Protocols (published)
doi.org
Aligning artificial intelligence with climate change mitigation
Lynn H. Kaack
Priya L. Donti
Emma Strubell
George Yoshito Kamiya
Felix Creutzig
David Rolnick
2022-06-01
Nature Climate Change (published)
doi.org
Conjugate Adder Net (CAddNet) - a Space-Efficient Approximate CNN
Lulan Shen
Maryam Ziaeefard
Brett Meyer
Warren Gross
James J. Clark
The AdderNet was recently developed as a way to implement deep neural networks without needing multiplication operations to combine weights … (see more)and inputs. Instead, absolute values of the difference between weights and inputs are used, greatly reducing the gate-level implementation complexity. Training of AdderNets is challenging, however, and the loss curves during training tend to fluctuate significantly. In this paper we propose the Conjugate Adder Network, or CAddNet, which uses the difference between the absolute values of conjugate pairs of inputs and the weights. We show that this can be implemented simply via a single minimum operation, resulting in a roughly 50% reduction in logic gate complexity as compared with AdderNets. The CAddNet method also stabilizes training as compared with AdderNets, yielding training curves similar to standard CNNs.
2022-06-01
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (published)
doi.org