Publications

Generalization of deep learning models for hepatic steatosis grading using B-mode ultrasound images

Pedro Vianna

Yue Qi

Michael Chassé

Guy Wolf

Eugene Belilovsky

An Tang

Guy Cloutier

2024-03-01

The Journal of the Acoustical Society of America (published)

doi.org

Heterogeneous ensemble prediction model of CO emission concentration in municipal solid waste incineration process using virtual data and real data hybrid-driven

Runyu Zhang

Jian Tang

Heng Xia

Jiakun Chen

Wen Yu

JunFei Qiao

2024-03-01

Journal of Cleaner Production (published)

doi.org

Implications of conscious AI in primary healthcare

Dorsai Ranjbari

Samira Abbasgholizadeh-Rahimi

2024-03-01

Family Medicine and Community Health (published)

doi.org

Iterative Graph Self-Distillation

Hanlin Zhang

Shuai Lin

Weiyang Liu

Pan Zhou

Jian Tang

Xiaodan Liang

Eric P. Xing

Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a met… (see more)hod called Iterative Graph Self-Distillation (IGSD) which learns graph-level representation in an unsupervised manner through instance discrimination using a self-supervised contrastive learning approach. IGSD involves a teacher-student distillation process that uses graph diffusion augmentations and constructs the teacher model using an exponential moving average of the student model. The intuition behind IGSD is to predict the teacher network representation of the graph pairs under different augmented views. As a natural extension, we also apply IGSD to semi-supervised scenarios by jointly regularizing the network with both supervised and self-supervised contrastive loss. Finally, we show that fine-tuning the IGSD-trained models with self-training can further improve graph representation learning. Empirically, we achieve significant and consistent performance gain on various graph datasets in both unsupervised and semi-supervised settings, which well validates the superiority of IGSD.

2024-03-01

IEEE Transactions on Knowledge and Data Engineering (published)

doi.org

openreview.net

Neural network prediction of the effect of thermomechanical controlled processing on mechanical properties

Sushant Sinha

Denzel Guye

Xiaoping Ma

Kashif Rehman

S. Yue

Narges Armanfard

2024-03-01

Machine Learning with Applications (published)

doi.org

Novel community data in ecology-properties and prospects.

Florian Hartig

Nerea Abrego

Alex Bush

Jonathan M. Chase

G. Guillera‐Arroita

M. Leibold

Otso T. Ovaskainen

Loïc Pellissier

Maximilian Pichler

Giovanni Poggiato

Laura J. Pollock

Sara Si-moussi

Wilfried Thuiller

Duarte S Viana

D. Warton

Damaris Zurell

Douglas W. Yu

2024-03-01

Trends in Ecology & Evolution (published)

doi.org

arxiv.org

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review.

Vania Karami

Mark J. Yaffe

Genevieve Gore

AJung Moon

Samira Abbasgholizadeh-Rahimi

2024-03-01

Archives of gerontology and geriatrics (Print) (published)

doi.org

Sources of richness and ineffability for phenomenally conscious states

Xu Ji

Eric Elmoznino

George Deane

Axel Constant

Guillaume Dumas

Guillaume Lajoie

Jonathan Simon

Yoshua Bengio

2024-03-01

Neuroscience of Consciousness (published)

doi.org

arxiv.org

The « jingle-jangle fallacy » of empathy: Delineating affective, cognitive and motor components of empathy from behavioral synchrony using a virtual agent

Julia Ayache

Guillaume Dumas

Alexander Sumich

D. Kuss

Darren Rhodes

Nadja Heym

2024-03-01

Personality and Individual Differences (published)

doi.org

COSMIC: Mutual Information for Task-Agnostic Summarization Evaluation

Maxime DARRIN

Philippe Formont

Jackie Chi Kit Cheung

Pablo Piantanida

Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that as… (see more)sesses summarizers based on their capacity to produce summaries that are useful for downstream tasks, while preserving task outcomes. We theoretically establish a direct relationship between the resulting error probability of these tasks and the mutual information between source texts and generated summaries. We introduce

2024-02-29

ArXiv (preprint)

doi.org

arxiv.org

Crowdkeeping in Last-mile Delivery

Xin Wang

Okan Arslan

Erick Delage

2024-02-29

Transportation Science (published)

doi.org

Disentangling the Causes of Plasticity Loss in Neural Networks