Publications

Derivation and validation of indices incorporating vasopressor dose and blood pressure values over time

Alain Gervais

François Lamontagne

Jean-Baptiste Michaud

KJ Neill

Adhikari

Jean-Michel Pagé

Marie-Hélène Masse

Michael O. Harhay

Michael Chassé

Félix Lamontagne

Katia Laforge

Alexandra Fortin

Marc-André Leclair

Simon Lévesque

Marie-Pier Domingue

Neda Momenzadeh

Martin Vallières

Ruxandra Pinto

Maxime Morin-Lavoie

Francis Carter … (voir 2 de plus)

Félix Camirand Lemyre

MD MSc. François Lamontagne

Rationale The blood pressure value below which the benefits of vasopressors clearly outweigh their disadvantages is uncertain. Objectives Th… (voir plus)e main objective of this analysis was to investigate the statistical properties and potential utility of indices estimating the vasopressor dose-rates as a function of blood pressure values over time. Methods In this single-center observational study, we collected blood pressure values from intensive care unit (ICU) monitors and norepinephrine dose-rates from infusion pumps corresponding to a derivation and a validation cohort. Patients included in each cohort were 18 years or older and received norepinephrine in the ICU. We defined and derived indices corresponding to vasopressor therapy above (>65 mmHg) and below (60 mmHg) targets. We report the distribution of both indices over time from both cohorts as well as their associations with hospital mortal

2024-09-22

medRxiv (prépublication)

doi.org

Not Only the Last-Layer Features for Spurious Correlations: All Layer Deep Feature Reweighting

Humza Wajid Hameed

Geraldin Nanfack

Eugene Belilovsky

Spurious correlations are a major source of errors for machine learning models, in particular when aiming for group-level fairness. It has b… (voir plus)een recently shown that a powerful approach to combat spurious correlations is to re-train the last layer on a balanced validation dataset, isolating robust features for the predictor. However, key attributes can sometimes be discarded by neural networks towards the last layer. In this work, we thus consider retraining a classifier on a set of features derived from all layers. We utilize a recently proposed feature selection strategy to select unbiased features from all the layers. We observe this approach gives significant improvements in worst-group accuracy on several standard benchmarks.

2024-09-22

ArXiv (prépublication)

doi.org

arxiv.org

Protein Language Models: Is Scaling Necessary?

Quentin Fournier

Robert M. Vernon

Almer Van Der Sloot

Benjamin Schulz

Sarath Chandar

Christopher James Langmead

Public protein sequence databases contain samples from the fitness landscape explored by nature. Protein language models (pLMs) pre-trained … (voir plus)on these sequences aim to capture this landscape for tasks like property prediction and protein design. Following the same trend as in natural language processing, pLMs have continuously been scaled up. However, the premise that scale leads to better performance assumes that source databases provide an accurate representation of the underlying fitness landscape, which is likely false. By developing an efficient codebase, designing a modern architecture, and addressing data quality concerns such as sample bias, we introduce AMPLIFY, a best-in-class pLM that is orders of magnitude less expensive to train and deploy than previous models. Furthermore, to support the scientific community and democratize the training of pLMs, we have open-sourced AMPLIFY’s pre-training codebase, data, and model checkpoints.

2024-09-22

bioRxiv (prépublication)

doi.org

Self Supervised Dictionary Learning Using Kernel Matching

Shubham Choudhary

Paul Masset

Demba Ba

We introduce a self supervised framework for learning representations in the context of dictionary learning. We cast the problem as a kernel… (voir plus) matching task between the input and the representation space, with constraints on the latent kernel. By adjusting these constraints, we demonstrate how the framework can adapt to different learning objectives. We then formulate a novel Alternate Direction Method of Multipli-ers (ADMM) based algorithm to solve the optimization problem and connect the dynamics to classical alternate minimization techniques. This approach offers a unique way of learning representations with kernel constraints, that enable us implicitly learn a generative map for the data from the learned representations which can have broad applications in representation learning tasks both in machine learning and neuro-science.

2024-09-21

International Workshop on Machine Learning for Signal Processing (publié)

doi.org

Al content detection in the emerging information ecosystem: new obligations for media and tech companies

Alistair Knott

Dino Pedreschi

Toshiya Jitsuzumi

Susan Leavy

David Eyers

Tapabrata Chakraborti

Andrew Trotman

Sundar Sundareswaran

Ricardo Baeza-Yates

Przemyslaw Biecek

Adrian Weller

Paul D. Teal

Subhadip Basu

Mehmet Haklidir

Virginia Morini

Stuart Russell

Yoshua Bengio

2024-09-20

Ethics and Information Technology (publié)

doi.org

ToxiSight: Insights Towards Detected Chat Toxicity

Zachary Yang

Domenico Tullo

Reihaneh Rabbany

We present a comprehensive explainability dashboard designed for in-game chat toxicity. This dashboard integrates various existing explainab… (voir plus)le AI (XAI) techniques, including token importance analysis, model output visualization, and attribution to the training dataset. It also provides insights through the closest positive and negative examples, facilitating a deeper understanding and potential correction of the training data. Additionally, the dashboard includes word sense analysis—particularly useful for new moderators—and offers free-text explanations for both positive and negative predictions. This multi-faceted approach enhances the interpretability and transparency of toxicity detection models.

2024-09-20

EMNLP/2024/Workshop/BlackBoxNLP (accepté)

openreview.net