Publications

General anaesthesia reduces the uniqueness of brain connectivity across individuals and across species
Andrea I. Luppi
Daniel Golkowski
Andreas Ranft
Rudiger Ilg
Denis Jordan
Danilo Bzdok
Adrian M. Owen
Lorina Naci
Emmanuel A. Stamatakis
Enrico Amico
Bratislav Misic
The human brain is characterised by idiosyncratic patterns of spontaneous thought, rendering each brain uniquely identifiable from its neura… (voir plus)l activity. However, deep general anaesthesia suppresses subjective experience. Does it also suppress what makes each brain unique? Here we used functional MRI under the effects of the general anaesthetics sevoflurane and propofol to determine whether anaesthetic-induced unconsciousness diminishes the uniqueness of the human brain: both with respect to the brains of other individuals, and the brains of another species. We report that under anaesthesia individual brains become less self-similar and less distinguishable from each other. Loss of distinctiveness is highly organised: it co-localises with the archetypal sensory-association axis, correlating with genetic and morphometric markers of phylogenetic differences between humans and other primates. This effect is more evident at greater anaesthetic depths, reproducible across sevoflurane and propofol, and reversed upon recovery. Providing convergent evidence, we show that under anaesthesia the functional connectivity of the human brain becomes more similar to the macaque brain. Finally, anaesthesia diminishes the match between spontaneous brain activity and meta-analytic brain patterns aggregated from the NeuroSynth engine. Collectively, the present results reveal that anaesthetised human brains are not only less distinguishable from each other, but also less distinguishable from the brains of other primates, with specifically human-expanded regions being the most affected by anaesthesia.
2023-11-12
bioRxiv (prépublication)
doi.org
Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games
Arjun Vaithilingam Sudhakar
Prasanna Parthasarathi
Janarthanan Rajendran
Sarath Chandar
2023-11-12
arXiv (prépublication)
doi.org
arxiv.org
Towards Climate Variable Prediction with Conditioned Spatio-Temporal Normalizing Flows
Christina Winkler
David Rolnick
2023-11-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Shiva Kanth Sujit
Pedro Braga
Jörg Bornschein
S Ebrahimi Kahou
Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from … (voir plus)scalar reward signals. A crucial challenge for current deep RL algorithms is that they require a tremendous amount of environment interactions for learning. This can be infeasible in situations where such interactions are expensive, such as in robotics. Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning. While online RL algorithms are typically evaluated as a function of the number of environment interactions, there isn't a single established protocol for evaluating offline RL methods. In this paper, we propose a sequential approach to evaluate offline RL algorithms as a function of the training set size and thus by their data efficiency. Sequential evaluation provides valuable insights into the data efficiency of the learning process and the robustness of algorithms to distribution changes in the dataset while also harmonizing the visualization of the offline and online learning phases. Our approach is generally applicable and easy to implement. We compare several existing offline RL algorithms using this approach and present insights from a variety of tasks and offline datasets.
2023-11-09
TMLR (accepté)
doi.org
openreview.net
CD3ζ ITAMs enable ligand discrimination and antagonism by inhibiting TCR signaling in response to low-affinity peptides
Guillaume Gaud
Sooraj Achar
François X. P. Bourassa
John Davies
Teri Hatzihristidis
Seeyoung Choi
Taisuke Kondo
Selamawit Gossa
Jan Lee
Paul Juneau
Naomi Taylor
Christian S. Hinrichs
Dorian B. McGavern
Paul François
Grégoire Altan‐Bonnet
Paul E. Love
2023-11-08
Nature Immunology (inconnu)
doi.org
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize
Ryan D'Orazio
Nicolas Loizou
Issam Hadj Laradji
Ioannis Mitliagkas
We investigate the convergence of stochastic mirror descent (SMD) under interpolation in relatively smooth and smooth convex optimization. I… (voir plus)n relatively smooth convex optimization we provide new convergence guarantees for SMD with a constant stepsize. For smooth convex optimization we propose a new adaptive stepsize scheme --- the mirror stochastic Polyak stepsize (mSPS). Notably, our convergence results in both settings do not make bounded gradient assumptions or bounded variance assumptions, and we show convergence to a neighborhood that vanishes under interpolation. Consequently, these results correspond to the first convergence guarantees under interpolation for the exponentiated gradient algorithm for fixed or adaptive stepsizes. mSPS generalizes the recently proposed stochastic Polyak stepsize (SPS) (Loizou et al. 2021) to mirror descent and remains both practical and efficient for modern machine learning applications while inheriting the benefits of mirror descent. We complement our results with experiments across various supervised learning tasks and different instances of SMD, demonstrating the effectiveness of mSPS.
2023-11-07
TMLR (accepté)
openreview.net
openreview.net
Capture the Flag: Uncovering Data Insights with Large Language Models.
Issam H. Laradji
Perouz Taslakian
Sai Rajeswar
Valentina Zantedeschi
Alexandre Lacoste
Nicolas Chapados
David Vázquez
Christopher Pal
Alexandre Drouin
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. Howev… (voir plus)er, accomplishing this task requires considerable technical skills, domain expertise, and human labor. This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data, leveraging recent advances in reasoning and code generation techniques. We propose a new evaluation methodology based on a "capture the flag" principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset. We further propose two proof-of-concept agents, with different inner workings, and compare their ability to capture such flags in a real-world sales dataset. While the work reported here is preliminary, our results are sufficiently interesting to mandate future exploration by the community.
2023-11-06
NeurIPS.cc/2023/Workshop/FMDM (publié)
doi.org
openreview.net
The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
Rim Assouel
Tom Marty
Massimo Caccia
Issam Hadj Laradji
Alexandre Drouin
Sai Rajeswar
Hector Palacios
Quentin Cappart
David Vázquez
Nicolas Chapados
Maxime Gasse
Alexandre Lacoste
2023-11-06
NeurIPS.cc/2023/Workshop/FMDM (publié)
openreview.net
openreview.net
30×30 biodiversity gains rely on national coordination
Isaac Eckert
Andrea Brown
Dominique Caron
Federico Riva
Laura J. Pollock
2023-11-05
Nature Communications (publié)
doi.org
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs
Shenyang Huang
Samy Coulombe
Yasmeen Hitti
Reihaneh Rabbany
Guillaume Rabusseau
Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly dete… (voir plus)ction in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address three main challenges associated with this problem: i). how to compare graph snapshots across time, ii). how to capture temporal dependencies, and iii). how to combine different views of a temporal graph. To solve the above challenges, we first propose Laplacian Anomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low dimensional embedding of the graph structure at each snapshot. LAD explicitly models short term and long term dependencies by applying two sliding windows. Next, we propose MultiLAD, a simple and effective generalization of LAD to multi-view graphs. MultiLAD provides the first change point detection method for multi-view dynamic graphs. It aggregates the singular values of the normalized graph Laplacian from different views through the scalar power mean operation. Through extensive synthetic experiments, we show that i). LAD and MultiLAD are accurate and outperforms state-of-the-art baselines and their multi-view extensions by a large margin, ii). MultiLAD's advantage over contenders significantly increases when additional views are available, and iii). MultiLAD is highly robust to noise from individual views. In five real world dynamic graphs, we demonstrate that LAD and MultiLAD identify significant events as top anomalies such as the implementation of government COVID-19 interventions which impacted the population mobility in multi-view traffic networks.
2023-11-05
ACM Transactions on Knowledge Discovery from Data (publié)
doi.org
arxiv.org
Coordination among leaf and fine root traits across a strong natural soil fertility gradient
Xavier Guilbeault-Mayers
Hans Lambers
Étienne Laliberté
2023-11-04
bioRxiv (prépublication)
doi.org
Player-Guided AI outperforms standard AI in Sequence Alignment Puzzles.
Renata Mutalova
Roman Sarrazin-Gendron
Parham Ghasemloo Gheidari
Eddie Cai
Gabriel Richard
Sébastien Caisse
Rob Knight
Mathieu Blanchette
Attila Szantner
Jérôme Waldispühl
Although Artificial Intelligence (AI) has gained widespread popularity across different fields, it is essential to recognize that AI systems… (voir plus), while impressive, do not consistently exhibit robust generalization, particularly for difficult problems such as the Multiple Sequence Alignment (MSA). In this study, we focus on bridging this performance gap by integrating human solutions into AI training. To illustrate these principles, we leverage data from Borderlands Science, a popular citizen science game in which small instances of the MSA problem are represented as puzzles. Our goal is to leverage the collective intelligence of human players to enhance the capabilities of AI agents. To achieve this, we have developed a Player-guided AI system that enables the AI model to learn from both standard training processes and the solutions provided by players. Our findings demonstrate that incorporating human-annotated information into the AI model improves its performance on puzzle tasks. Furthermore, the Player-guided AI model shows a decrease in noise compared to a pure AI model. This advancement allows for leveraging the model to align new sequences with improved accuracy and effectiveness. Moreover, this research brings attention to the potential of integrating AI and human expertise to address other challenges where the performance of AI models may be unsatisfactory.
2023-11-04
International Conference on Climate Informatics (publié)
doi.org