Publications

Iterative Monte Carlo Tree Search for Neural Architecture Search

Mehraveh Javan

Matthew Toews

Marco Pedersoli

2025-11-10

Proceedings of the Fourth International Conference on Automated Machine Learning (publié)

proceedings.mlr.press

LT-Soups: Bridging Head and Tail Classes via Subsampled Model Soups

Masih Aminbeidokhti

Subhankar Roy

Eric Granger

Elisa Ricci

Marco Pedersoli

Real-world datasets typically exhibit long-tailed (LT) distributions, where a few head classes dominate and many tail classes are severely u… (voir plus)nderrepresented. While recent work shows that parameter-efficient fine-tuning (PEFT) methods like LoRA and AdaptFormer preserve tail-class performance on foundation models such as CLIP, we find that they do so at the cost of head-class accuracy. We identify the head-tail ratio, the proportion of head to tail classes, as a crucial but overlooked factor influencing this trade-off. Through controlled experiments on CIFAR100 with varying imbalance ratio (

2025-11-10

ArXiv (prépublication)

doi.org

arxiv.org

Observations of AGN-driven feedback: dynamics and ionization of the filaments in M87

Camille Poitras

Marie-Lou Gendron-Marsolais

Valeria Olivares

Yuan Li

Adrien Picquenot

Aurora Simionescu

Matteo Fossati

Alessandro Boselli

Laura Hermosa Muñoz

Sara Cazzoli

Julie Hlavacek-Larrondo

Annabelle Richard-Laferrière

We present a comprehensive kinematic and ionization analysis of the warm ionized filaments (…

2025-11-10

Monthly Notices of the Royal Astronomical Society (publié)

doi.org

arxiv.org

Adaptive Group Robust Ensemble Knowledge Distillation

Patrik Joslin Kenfack

Ulrich Aivodji

Samira Ebrahimi Kahou

Neural networks can learn spurious correlations in the data, often leading to performance disparity for underrepresented subgroups. Studies … (voir plus)have demonstrated that the disparity is amplified when knowledge is distilled from a complex teacher model to a relatively"simple"student model. Prior work has shown that ensemble deep learning methods can improve the performance of the worst-case subgroups; however, it is unclear if this advantage carries over when distilling knowledge from an ensemble of teachers, especially when the teacher models are debiased. This study demonstrates that traditional ensemble knowledge distillation can significantly drop the performance of the worst-case subgroups in the distilled student model even when the teacher models are debiased. To overcome this, we propose Adaptive Group Robust Ensemble Knowledge Distillation (AGRE-KD), a simple ensembling strategy to ensure that the student model receives knowledge beneficial for unknown underrepresented subgroups. Leveraging an additional biased model, our method selectively chooses teachers whose knowledge would better improve the worst-performing subgroups by upweighting the teachers with gradient directions deviating from the biased model. Our experiments on several datasets demonstrate the superiority of the proposed ensemble distillation technique and show that it can even outperform classic model ensembles based on majority voting.

2025-11-09

Transactions on Machine Learning Research (accepté)

doi.org

openreview.net

Comparability of Canadian SARS-CoV-2 seroprevalence estimates with statistical adjustment for socio-demographic representation

Yuan Yu

Jiacheng Chen

Matthew J. Knight

Sheila F. O’Brien

David L. Buckeridge

Carmen L. Charlton

W. Alton Russell

OBJECTIVE SARS-CoV-2 serological surveillance used blood donors, research cohorts, and residual patient samples. Differences in socio-demogr… (voir plus)aphic characteristics across these sources may bias seroprevalence estimates, necessitating statistical adjustment. METHODS We re-analyzed data from six serosurveillance sources, comparing the estimated percent of the population positive for SARS-CoV-2 anti-nucleocapsid antibodies for six regions during periods when the sources' sample collection overlapped. We assessed the concordance between sources with and without using multilevel regression and poststratification (MRP) to adjust for differences in representation by age, sex, and race. RESULTS Across regions and timepoints, unadjusted seroprevalence differed between sources by up to 20%. MRP did not consistently improve comparability of seroprevalence across sources. In 2022, seroprevalence was consistently highest among blood donors, and MRP increased regional seroprevalence across all sources (except in Manitoba during January-April 2022 in ABC Study). In a secondary regression analysis, immunoassay kit and sample type (dried blood spot or venous blood draw) strongly influenced the odds that a sample was classified as seropositive. CONCLUSION Adjusting for representativeness using common socio-demographic variables did not systematically improve concordance in seropositivity estimates between serosurveillance sources. While discrepancies between sources might be influenced by studies' representativeness of characteristics we did not assess, methods for measuring seropositivity appear to explain much of the differences between sources. Serosurveillance findings are influenced by many aspects of study design beyond representativeness, such as sample type (venous blood draw or dried blood spots), choice of immunoassay, and laboratory procedures such as dilution or immunoassay calibration.

2025-11-09

Canadian Journal of Public Health (publié)

doi.org

Efficient, Non‐Destructive Transfer of Wafer‐Scale Monolayer MoS
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by Interface Engineering

Zheng Wei

Yongqing Cai

Jieying Liu

Liyan Zhang

Jiaojiao Zhao

Jian Tang

Li Li

Qinqin Wang

Huimin Zhang

Zhihua Zhang

Dongxia Shi

Luojun Du

2025-11-09

Advanced Functional Materials (publié)

doi.org

Identifying indicators of consciousness in AI systems

Patrick Butlin

Robert Long

Tim Bayne

Yoshua Bengio

Jonathan Birch

David Chalmers

Axel Constant

George Deane

Eric Elmoznino

Stephen M. Fleming

Xu Ji

Ryota Kanai

Colin Klein

Grace Lindsay

Matthias Michel

Liad Mudrik

Megan A.K. Peters

Eric Schwitzgebel

Jonathan Simon

Rufin Vanrullen

2025-11-09

Trends in Cognitive Sciences (publié)

doi.org

Top-down feedback matters: Functional impact of brainlike connectivity motifs on audiovisual integration

Mashbayar Tugsbayar

Mingze Li

Eilif B Muller

Blake Richards

Artificial neural networks (ANNs) are an important tool for studying neural computation, but many features of the brain are not captured by … (voir plus)standard ANN architectures. One notable missing feature in most ANN models is top-down feedback, i.e. projections from higher-order layers to lower-order layers in the network. Top-down feedback is ubiquitous in the brain, and it has a unique modulatory impact on activity in neocortical pyramidal neurons. However, we still do not understand its computational role. Here we develop a deep neural network model that captures the core functional properties of top-down feedback in the neocortex, allowing us to construct hierarchical recurrent ANN models that more closely reflect the architecture of the brain. We use this to explore the impact of different hierarchical recurrent architectures on an audiovisual integration task. We find that certain hierarchies, namely those that mimic the architecture of the human brain, impart ANN models with a light visual bias similar to that seen in humans. This bias does not impair performance on the audiovisual tasks. The results further suggest that different configurations of top-down feedback make otherwise identically connected models functionally distinct from each other, and from traditional feedforward and laterally recurrent models. Altogether our findings demonstrate that modulatory top-down feedback is a computationally relevant feature of biological brains, and that incorporating it into ANNs affects their behavior and constrains the solutions it’s likely to discover.

2025-11-09

BioRxiv (prépublication)

doi.org

Understanding the role of depth in the neural tangent kernel for overparameterized neural networks

William St-Arnaud

Margarida Carvalho

Golnoosh Farnadi

2025-11-09

ArXiv (prépublication)

doi.org

arxiv.org

Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages