Publications

AI4GCC - Track 3: Consumption and the Challenges of Multi-Agent RL
Marco Jiralerspong
Gauthier Gidel
2023-08-09
ArXiv (prépublication)
doi.org
arxiv.org
Teacher-Student Architecture for Knowledge Distillation: A Survey
Chengming Hu
Xuan Li
Danyang Liu
Haolun Wu
Xi Chen
Ju Wang
Xue (Steve) Liu
Although Deep neural networks (DNNs) have shown a strong capacity to solve large-scale problems in many areas, such DNNs are hard to be depl… (voir plus)oyed in real-world systems due to their voluminous parameters. To tackle this issue, Teacher-Student architectures were proposed, where simple student networks with a few parameters can achieve comparable performance to deep teacher networks with many parameters. Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement. With the help of Teacher-Student architectures, current studies are able to achieve multiple distillation objectives through lightweight and generalized student networks. Different from existing KD surveys that primarily focus on knowledge compression, this survey first explores Teacher-Student architectures across multiple distillation objectives. This survey presents an introduction to various knowledge representations and their corresponding optimization objectives. Additionally, we provide a systematic overview of Teacher-Student architectures with representative learning algorithms and effective distillation schemes. This survey also summarizes recent applications of Teacher-Student architectures across multiple purposes, including classification, recognition, generation, ranking, and regression. Lastly, potential research directions in KD are investigated, focusing on architecture design, knowledge quality, and theoretical studies of regression-based learning, respectively. Through this comprehensive survey, industry practitioners and the academic community can gain valuable insights and guidelines for effectively designing, learning, and applying Teacher-Student architectures on various distillation objectives.
2023-08-08
ArXiv (prépublication)
doi.org
arxiv.org
Bayesian modeling disentangles language versus executive control disruption in stroke
Gesa Hartwigsen
Jae‐Sung Lim
Hee-Joon Bae
Kyung‐Ho Yu
Hugo J. Kuijf
Nick A. Weaver
J. Matthijs Biesbroek
Jakub Kopal
Danilo Bzdok
2023-08-07
bioRxiv (prépublication)
doi.org
Exploring Security Practices in Infrastructure as Code: An Empirical Study
Alexandre Verdet
Mohammad Hamdaqa
Léuson M. P. Da Silva
Foutse Khomh
Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools, allowing the community to convenientl… (voir plus)y manage and configure cloud infrastructure using scripts. However, the scripting process itself does not automatically prevent practitioners from introducing misconfigurations, vulnerabilities, or privacy risks. As a result, ensuring security relies on practitioners understanding and the adoption of explicit policies, guidelines, or best practices. In order to understand how practitioners deal with this problem, in this work, we perform an empirical study analyzing the adoption of IaC scripted security best practices. First, we select and categorize widely recognized Terraform security practices promulgated in the industry for popular cloud providers such as AWS, Azure, and Google Cloud. Next, we assess the adoption of these practices by each cloud provider, analyzing a sample of 812 open-source projects hosted on GitHub. For that, we scan each project configuration files, looking for policy implementation through static analysis (checkov). Additionally, we investigate GitHub measures that might be correlated with adopting these best practices. The category Access policy emerges as the most widely adopted in all providers, while Encryption in rest are the most neglected policies. Regarding GitHub measures correlated with best practice adoption, we observe a positive, strong correlation between a repository number of stars and adopting practices in its cloud infrastructure. Based on our findings, we provide guidelines for cloud practitioners to limit infrastructure vulnerability and discuss further aspects associated with policies that have yet to be extensively embraced within the industry.
2023-08-07
ArXiv (prépublication)
doi.org
arxiv.org
Toward computing attributions for dimensionality reduction techniques
Matthew Scicluna
Jean-Christophe Grenier
Raphael Poujol
Sébastien Lemieux
Julie Hussin
2023-08-03
Bioinformatics Advances (publié)
doi.org
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Jose Gonz'alez-Abad
'Alex Hern'andez-Garc'ia
Paula Harder
David Rolnick
Jos'e Manuel Guti'errez
2023-08-02
ArXiv (prépublication)
doi.org
arxiv.org
Are vividness judgments in mental imagery correlated with perceptual thresholds?
Ian Charest
Clémence Bertrand Pilon
Hugo Delhaye
Vincent Taschereau-Dumouchel
Frédéric Gosselin
2023-08-01
Journal of Vision (publié)
doi.org
Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data
Philipp Thölke
Yorguin-José Mantilla-Ramos
Hamza Abdelhedi
Charlotte Maschke
Arthur Dehgan
Yann Harel
Anirudha Kemtur
Loubna Mekki Berrada
Myriam Sahraoui
Tammy Young
Antoine Bellemare Pépin
Clara El Khantour
Mathieu Landry
Annalisa Pascarella
Vanessa Hadid
Etienne Combrisson
Jordan O’Byrne
Karim Jerbi
2023-08-01
NeuroImage (publié)
doi.org
Consultative engagement of stakeholders toward a roadmap for African language technologies
Kathleen Siminyu
Jade Abbott
Kọ́lá Túbọ̀sún
Aremu Anuoluwapo
Blessing Kudzaishe Sibanda
Kofi Yeboah
David Ifeoluwa Adelani
Masabata Mokgesi-Selinga
Frederick R. Apina
Angela Thandizwe Mthembu
Arshath Ramkilowan
Babatunde Oladimeji
2023-08-01
Patterns (publié)
doi.org
A cop-winning strategy on strongly cop-win graphs
Jos'ee Desharnais
François Laviolette
Héli Marcoux
Norbert Polat
2023-08-01
Discrete Mathematics (publié)
doi.org
Decentralized Linear Quadratic Systems With Major and Minor Agents and Non-Gaussian Noise
Mohammad Afshari
Aditya Mahajan
A decentralized linear quadratic system with a major agent and a collection of minor agents is considered. The major agent affects the minor… (voir plus) agents, but not vice versa. The state of the major agent is observed by all agents. In addition, the minor agents have a noisy observation of their local state. The noise process is not assumed to be Gaussian. The structures of the optimal strategy and the best linear strategy are characterized. It is shown that the major agent's optimal control action is a linear function of the major agent's minimum mean-squared error (MMSE) estimate of the system state while the minor agent's optimal control action is a linear function of the major agent's MMSE estimate of the system state and a “correction term” that depends on the difference of the minor agent's MMSE estimate of its local state and the major agent's MMSE estimate of the minor agent's local state. Since the noise is non-Gaussian, the minor agent's MMSE estimate is a nonlinear function of its observation. It is shown that replacing the minor agent's MMSE estimate with its linear least mean square estimate gives the best linear control strategy. The results are proved using a direct method based on conditional independence, common-information-based splitting of state and control actions, and simplifying the per-step cost based on conditional independence, orthogonality principle, and completion of squares.
2023-08-01
IEEE Transactions on Automatic Control (publié)
doi.org
arxiv.org
Determinants of Access to Essential Surgery in the Democratic Republic of Congo
Luc Malemo Kalisya
Ava Yap
Boniface Mitume
Christian Salmon
Kambale Karafuli
Dan Poenaru
Rosebella Onyango
2023-08-01
Journal of Surgical Research (publié)
doi.org