Publications

Challenges for impact evaluation of WHO’s normative output

Catherine Régis

Gaelle Foucault

Jean-Louis Denis

Pierre Larouche

Miriam Cohen

2024-10-01

Bulletin of the World Health Organization (publié)

doi.org

A Data-driven Discovery of the Causal Connection between Galaxy and Black Hole Evolution

Zehao Jin

Mario Pasquato

Benjamin L. Davis

Tristan Deleu

Yu Luo

Changhyun Cho

Pablo Lemos

Laurence Perreault-Levasseur

Yoshua Bengio

Xi Kang

Andrea Maccio

Yashar Hezaveh

2024-10-01

ArXiv (prépublication)

arxiv.org

Inferring electric vehicle charging patterns from smart meter data for impact studies

Feng Li

Élodie Campeau

Ilhan Kocar

Antoine Lesage-Landry

2024-10-01

Electric power systems research (publié)

doi.org

Innovative transfusion strategies for blood deserts in disaster settings

Shreenik Kundu

Ayla Gerk

Robert Glatter

Dan Poenaru

2024-10-01

BMJ Global Health (publié)

doi.org

MAP: Model Merging with Amortized Pareto Front Using Limited Computation

Lu Li

Tianyu Zhang

Zhiqi Bu

Suyuchen Wang

Huan He

Jie Fu

Yonghui Wu

Jiang Bian

Yong Chen

Yoshua Bengio

2024-10-01

NeurIPS.cc/2024/Workshop/Federated_Learning (présentation orale)

openreview.net

Single-Shot Learning of Stable Dynamical Systems for Long-Horizon Manipulation Tasks

Alexandre St-Aubin

Amin Abyaneh

Hsiu-Chin Lin

Mastering complex sequential tasks continues to pose a significant challenge in robotics. While there has been progress in learning long-hor… (voir plus)izon manipulation tasks, most existing approaches lack rigorous mathematical guarantees for ensuring reliable and successful execution. In this paper, we extend previous work on learning long-horizon tasks and stable policies, focusing on improving task success rates while reducing the amount of training data needed. Our approach introduces a novel method that (1) segments long-horizon demonstrations into discrete steps defined by waypoints and subgoals, and (2) learns globally stable dynamical system policies to guide the robot to each subgoal, even in the face of sensory noise and random disturbances. We validate our approach through both simulation and real-world experiments, demonstrating effective transfer from simulation to physical robotic platforms. Code is available at https://github.com/Alestaubin/stable-imitation-policy-with-waypoints

2024-10-01

ArXiv (prépublication)

doi.org

arxiv.org

A Survey of Diversification Techniques in Search and Recommendation

Haolun Wu

Yansen Zhang

Chen Ma

Fuyuan Lyu

Bowei He

Fernando Diaz

Bhaskar Mitra

Xue (Steve) Liu

Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers … (voir plus)and the equal market exposure of providers. There has been a growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, the diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey’s main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the openness research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.

2024-10-01

IEEE Transactions on Knowledge and Data Engineering (publié)

doi.org

arxiv.org

Towards a unified XAI-based framework for digital forensic investigations

Zainab Khalid

Farkhund Iqbal

Benjamin Fung

2024-10-01

Forensic Science International: Digital Investigation (publié)

doi.org

The oneirogen hypothesis: modeling the hallucinatory effects of classical psychedelics in terms of replay-dependent plasticity mechanisms

Colin Bredenberg

Fabrice Normandin

Blake Richards

Guillaume Lajoie

2024-09-30

bioRxiv (prépublication)

doi.org

What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based Approach

Xingfang Wu

Heng Li

Foutse Khomh

Log data are generated from logging statements in the source code, providing insights into the execution processes of software applications … (voir plus)and systems. State-of-the-art log-based anomaly detection approaches typically leverage deep learning models to capture the semantic or sequential information in the log data and detect anomalous runtime behaviors. However, the impacts of these different types of information are not clear. In addition, existing approaches have not captured the timestamps in the log data, which can potentially provide more fine-grained temporal information than sequential information. In this work, we propose a configurable transformer-based anomaly detection model that can capture the semantic, sequential, and temporal information in the log data and allows us to configure the different types of information as the model's features. Additionally, we train and evaluate the proposed model using log sequences of different lengths, thus overcoming the constraint of existing methods that rely on fixed-length or time-windowed log sequences as inputs. With the proposed model, we conduct a series of experiments with different combinations of input features to evaluate the roles of different types of information in anomaly detection. When presented with log sequences of varying lengths, the model can attain competitive and consistently stable performance compared to the baselines. The results indicate that the event occurrence information plays a key role in identifying anomalies, while the impact of the sequential and temporal information is not significant for anomaly detection in the studied public datasets. On the other hand, the findings also reveal the simplicity of the studied public datasets and highlight the importance of constructing new datasets that contain different types of anomalies to better evaluate the performance of anomaly detection models.

2024-09-30

ArXiv (prépublication)

doi.org

arxiv.org

Automating MedSAM by Learning Prompts with Weak Few-Shot Supervision

Mélanie Gaillochet

Christian Desrosiers

Hervé Lombaert

2024-09-28

Lecture Notes in Computer Science (publié)

doi.org

arxiv.org