Publications

Oxygen thresholds in critically ill patients: need for personalized targets. Author's reply.

Guillaume Dumas

Laveena Munshi

2024-08-12

Intensive Care Medicine (published)

doi.org

Oxygen thresholds in critically ill patients: need for personalized targets. Author's reply.

Guillaume Dumas

Laveena Munshi

2024-08-12

Intensive Care Medicine (published)

doi.org

Cell Morphology-Guided Small Molecule Generation with GFlowNets

Stephen Zhewen Lu

Ziqing Lu

Ehsan Hajiramezanali

Tommaso Biancalani

Yoshua Bengio

Gabriele Scalia

Michał Koziarski

High-content phenotypic screening, including high-content imaging (HCI), has gained popularity in the last few years for its ability to char… (see more)acterize novel therapeutics without prior knowledge of the protein target. When combined with deep learning techniques to predict and represent molecular-phenotype interactions, these advancements hold the potential to significantly accelerate and enhance drug discovery applications. This work focuses on the novel task of HCI-guided molecular design. Generative models for molecule design could be guided by HCI data, for example with a supervised model that links molecules to phenotypes of interest as a reward function. However, limited labeled data, combined with the high-dimensional readouts, can make training these methods challenging and impractical. We consider an alternative approach in which we leverage an unsupervised multimodal joint embedding to define a latent similarity as a reward for GFlowNets. The proposed model learns to generate new molecules that could produce phenotypic effects similar to those of the given image target, without relying on pre-annotated phenotypic labels. We demonstrate that the proposed method generates molecules with high morphological and structural similarity to the target, increasing the likelihood of similar biological activity, as confirmed by an independent oracle model.

2024-08-09

ArXiv (preprint)

doi.org

arxiv.org

Revisiting Feature Prediction for Learning Visual Representations from Video

Adrien Bardes

Quentin Garrido

Jean Ponce

Xinlei Chen

Michael Rabbat

Yann LeCun

Mahmoud Assran

Nicolas Ballas

2024-08-09

TMLR (accepted)

doi.org

openreview.net

Cardinality Minimization, Constraints, and Regularization: A Survey

Andreas M. Tillmann

Daniel Bienstock

Andrea Lodi

Alexandra Schwartz

We survey optimization problems that involve the cardinality of variable vectors in constraints or the objective function. We provide a unif… (see more)ied viewpoint on the general problem classes and models, and give concrete examples from diverse application fields such as signal and image processing, portfolio selection, or machine learning. The paper discusses general-purpose modeling techniques and broadly applicable as well as problem-specific exact and heuristic solution approaches. While our perspective is that of mathematical optimization, a main goal of this work is to reach out to and build bridges between the different communities in which cardinality optimization problems are frequently encountered. In particular, we highlight that modern mixed-integer programming, which is often regarded as impractical due to commonly unsatisfactory behavior of black-box solvers applied to generic problem formulations, can in fact produce provably high-quality or even optimal solutions for cardinality optimization problems, even in large-scale real-world settings. Achieving such performance typically draws on the merits of problem-specific knowledge that may stem from different fields of application and, e.g., shed light on structural properties of a model or its solutions, or lead to the development of efficient heuristics; we also provide some illustrative examples.

2024-08-08

SIAM Review (published)

doi.org

arxiv.org

Stochastic Wiring of Cell Types Enhances Fitness by Generating Phenotypic Variability

Divyansha Lachi

Ann Huang

Augustine N. Mavor-Parker

Arna Ghosh

Blake Richards

Anthony Zador

The development of neural connectivity is a crucial biological process that gives rise to diverse brain circuits and behaviors. Neural devel… (see more)opment is a stochastic process, but this stochasticity is often treated as a nuisance to overcome rather than as a functional advantage. Here we use a computational model, in which connection probabilities between discrete cell types are genetically specified, to investigate the benefits of stochasticity in the development of neural wiring. We show that this model can be viewed as a generalization of a powerful class of artificial neural networks—Bayesian neural networks—where each network parameter is a sample from a distribution. Our results reveal that stochasticity confers a greater benefit in large networks and variable environments, which may explain its role in organisms with larger brains. Surprisingly, we find that the average fitness over a population of agents is higher than a single agent defined by the average connection probability. Our model reveals how developmental stochasticity, by inducing a form of non-heritable phenotypic variability, can increase the probability that at least some individuals will survive in rapidly changing, unpredictable environments. Our results suggest how stochasticity may be an important feature rather than a bug in neural development.

2024-08-08

bioRxiv (preprint)

doi.org

Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity

Charlotte Maschke

Jordan O’Byrne

Michele Angelo Colombo

Melanie Boly

Olivia Gosseries

Steven Laureys

Mario Rosanova

Karim Jerbi

Stefanie Blain-Moraes

2024-08-05

Communications Biology (published)

doi.org

Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity

Charlotte Maschke

Jordan O’Byrne

Michele Angelo Colombo

Melanie Boly

Olivia Gosseries

Steven Laureys

Mario Rosanova

Karim Jerbi

Stefanie Blain-Moraes

2024-08-05

Communications Biology (published)

doi.org

Critical dynamics in spontaneous EEG predict anesthetic-induced loss of consciousness and perturbational complexity

Charlotte Maschke

Jordan O’Byrne

Michele Angelo Colombo

Melanie Boly

Olivia Gosseries

Steven Laureys

Mario Rosanova

Karim Jerbi

Stefanie Blain-Moraes

2024-08-05

Communications Biology (published)

doi.org

Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods

Julien Ferry

Gabriel Laberge

Ulrich Aivodji

A hybrid model involves the cooperation of an interpretable model and a complex black box. At inference, any input of the hybrid model is as… (see more)signed to either its interpretable or complex component based on a gating mechanism. The advantages of such models over classical ones are two-fold: 1) They grant users precise control over the level of transparency of the system and 2) They can potentially perform better than a standalone black box since redirecting some of the inputs to an interpretable model implicitly acts as regularization. Still, despite their high potential, hybrid models remain under-studied in the interpretability/explainability literature. In this paper, we remedy this fact by presenting a thorough investigation of such models from three perspectives: Theory, Taxonomy, and Methods. First, we explore the theory behind the generalization of hybrid models from the Probably-Approximately-Correct (PAC) perspective. A consequence of our PAC guarantee is the existence of a sweet spot for the optimal transparency of the system. When such a sweet spot is attained, a hybrid model can potentially perform better than a standalone black box. Secondly, we provide a general taxonomy for the different ways of training hybrid models: the Post-Black-Box and Pre-Black-Box paradigms. These approaches differ in the order in which the interpretable and complex components are trained. We show where the state-of-the-art hybrid models Hybrid-Rule-Set and Companion-Rule-List fall in this taxonomy. Thirdly, we implement the two paradigms in a single method: HybridCORELS, which extends the CORELS algorithm to hybrid modeling. By leveraging CORELS, HybridCORELS provides a certificate of optimality of its interpretable component and precise control over transparency. We finally show empirically that HybridCORELS is competitive with existing hybrid models, and performs just as well as a standalone black box (or even better) while being partly transparent.

2024-08-05

TMLR (accepted)

doi.org

openreview.net

The effect of gestational age on short- and long-term complications following primary esophageal atresia repair

Mathias Johansen

Samuel Wasserman

Dan Poenaru

Jean Martin Laberge

Sam J. Daniel

Thomas Engelhardt

2024-08-03