Publications

Multi-Fidelity Active Learning with GFlowNets
Alex Hernández-García
Nikita Saxena
Moksh J. Jain
Cheng-Hao Liu
Yoshua Bengio
In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanw… (voir plus)hile, the progress in machine learning has turned it into a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems present challenges where current machine learning methods cannot yet efficiently leverage the available data and resources. For example, in scientific discovery, we are often faced with the problem of exploring very large, high-dimensional spaces, where querying a high fidelity, black-box objective function is very expensive. Progress in machine learning methods that can efficiently tackle such problems would help accelerate currently crucial areas such as drug and materials discovery. In this paper, we propose the use of GFlowNets for multi-fidelity active learning, where multiple approximations of the black-box function are available at lower fidelity and cost. GFlowNets are recently proposed methods for amortised probabilistic inference that have proven efficient for exploring large, high-dimensional spaces and can hence be practical in the multi-fidelity setting too. Here, we describe our algorithm for multi-fidelity active learning with GFlowNets and evaluate its performance in both well-studied synthetic tasks and practically relevant applications of molecular discovery. Our results show that multi-fidelity active learning with GFlowNets can efficiently leverage the availability of multiple oracles with different costs and fidelities to accelerate scientific discovery and engineering design.
2024-07-24
TMLR (accepté)
doi.org
openreview.net
Development of Error Passing Network for Optimizing the Prediction of VO$_2$ peak in Childhood Acute Leukemia Survivors
Nicolas Raymond
Hakima Laribi
Maxime Caru
Mehdi Mitiche
Valerie Marcil
Maja Krajinovic
Daniel Curnier
Daniel Sinnett
Martin Vallières
Approximately two-thirds of survivors of childhood acute lymphoblastic leukemia (ALL) cancer develop late adverse effects post-treatment. Pr… (voir plus)ior studies explored prediction models for personalized follow-up, but none integrated the usage of neural networks to date. In this work, we propose the Error Passing Network (EPN), a graph-based method that leverages relationships between samples to propagate residuals and adjust predictions of any machine learning model. We tested our approach to estimate patients’ \vo peak, a reliable indicator of their cardiac health. We used the EPN in conjunction with several baseline models and observed up to 12.16% improvement in the mean average percentage error compared to the last established equation predicting \vo peak in childhood ALL survivors. Along with this performance improvement, our final model is more efficient considering that it relies only on clinical variables that can be self-reported by patients, therefore removing the previous need of executing a resource-consuming physical test.
2024-07-23
Proceedings of the fifth Conference on Health, Inference, and Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
In value-based deep reinforcement learning, a pruned network is a good network
Johan Obando-Ceron
Aaron Courville
Pablo Samuel Castro
Recent work has shown that deep reinforcement learning agents have difficulty in effectively using their network parameters. We leverage pri… (voir plus)or insights into the advantages of sparse training techniques and demonstrate that gradual magnitude pruning enables value-based agents to maximize parameter effectiveness. This results in networks that yield dramatic performance improvements over traditional networks, using only a small fraction of the full network parameters.
2024-07-22
ICML (Accept (Poster))
doi.org
proceedings.mlr.press
A Waddington landscape for prototype learning in generalized Hopfield networks
Nacer Eddine Boukacem
Allen Leary
Robin Theriault
Felix Gottlieb
Madhav Mani
Paul François
Networks in machine learning offer examples of complex high-dimensional dynamical systems reminiscent of biological systems. Here, we study … (voir plus)the learning dynamics of Generalized Hopfield networks, which permit a visualization of internal memories. These networks have been shown to proceed through a 'feature-to-prototype' transition, as the strength of network nonlinearity is increased, wherein the learned, or terminal, states of internal memories transition from mixed to pure states. Focusing on the prototype learning dynamics of the internal memories we observe a strong resemblance to the canalized, or low-dimensional, dynamics of cells as they differentiate within a Waddingtonian landscape. Dynamically, we demonstrate that learning in a Generalized Hopfield Network proceeds through sequential 'splits' in memory space. Furthermore, order of splitting is interpretable and reproducible. The dynamics between the splits are canalized in the Waddington sense -- robust to variations in detailed aspects of the system. In attempting to make the analogy a rigorous equivalence, we study smaller subsystems that exhibit similar properties to the full system. We combine analytical calculations with numerical simulations to study the dynamical emergence of the feature-to-prototype transition, and the behaviour of splits in the landscape, saddles points, visited during learning. We exhibit regimes where saddles appear and disappear through saddle-node bifurcations, qualitatively changing the distribution of learned memories as the strength of the nonlinearity is varied -- allowing us to systematically investigate the mechanisms that underlie the emergence of Waddingtonian dynamics. Memories can thus differentiate in a predictive and controlled way, revealing new bridges between experimental biology, dynamical systems theory, and machine learning.
2024-07-22
Physical Review Research (publié)
doi.org
arxiv.org
Wasserstein Distributionally Robust Shallow Convex Neural Networks
Julien Pallage
Antoine Lesage-Landry
2024-07-22
ArXiv (prépublication)
doi.org
arxiv.org
A Rapid Method for Impact Analysis of Grid-Edge Technologies on Power Distribution Networks
Feng Li
Ilhan Kocar
Antoine Lesage-Landry
This paper presents a novel rapid estimation method (REM) to perform stochastic impact analysis of grid-edge technologies (GETs) to the powe… (voir plus)r distribution networks. The evolution of network states' probability density functions (PDFs) in terms of GET penetration levels are characterized by the Fokker-Planck equation (FPE). The FPE is numerically solved to compute the PDFs of network states, and a calibration process is also proposed such that the accuracy of the REM is maintained for large-scale distribution networks. The approach is illustrated on a large-scale realistic distribution network using a modified version of the IEEE 8500 feeder, where electric vehicles (EVs) or photovoltaic systems (PVs) are installed at various penetration rates. It is demonstrated from quantitative analyses that the results from our proposed approach have negligible errors comparing with those obtained from Monte Carlo simulations.
2024-07-20
2024 IEEE Power & Energy Society General Meeting (PESGM) (publié)
doi.org
T2VIndexer: A Generative Video Indexer for Efficient Text-Video Retrieval
Yili Li
Jing Yu
Keke Gai
Bang Liu
Gang Xiong
Qi Wu
Current text-video retrieval methods mainly rely on cross-modal matching between queries and videos to calculate their similarity scores, wh… (voir plus)ich are then sorted to obtain retrieval results. This method considers the matching between each candidate video and the query, but it incurs a significant time cost and will increase notably with the increase of candidates. Generative models are common in natural language processing and computer vision, and have been successfully applied in document retrieval, but their application in multimodal retrieval remains unexplored. To enhance retrieval efficiency, in this paper, we introduce a model-based video indexer named T2VIndexer, which is a sequence-to-sequence generative model directly generating video identifiers and retrieving candidate videos with constant time complexity. T2VIndexer aims to reduce retrieval time while maintaining high accuracy. To achieve this goal, we propose video identifier encoding and query-identifier augmentation approaches to represent videos as short sequences while preserving their semantic information. Our method consistently enhances the retrieval efficiency of current state-of-the-art models on four standard datasets. It enables baselines with only 30%-50% of the original retrieval time to achieve better retrieval performance on MSR-VTT (+1.0%), MSVD (+1.8%), ActivityNet (+1.5%), and DiDeMo (+0.2%). The code is available at https://anonymous.4open.science/r/T2VIndexer-40BE.
2024-07-19
acmmm.org/ACMMM/2024/Conference (présentation orale)
openreview.net
openreview.net
ANDES, the high resolution spectrograph for the ELT: science goals, project overview, and future developments
Alessandro Marconi
Artur R. Abreu
Vardan Adibekyan
Valentina Alberti
Simon Albrecht
Jailson Alcaniz
Matteo Aliverti
Carlos Allende Prieto
Julian Alvarado-Gomez
Catarina Alves
Pedro J. Amado
Manuel Amate
Michael Andersen
Simone Antoniucci
E. Artigau
Christophe Bailet
Clark E. Baker
Veronica Baldini
Andrea Balestra
S.A. Barnes … (voir 271 de plus)
Frédérique Baron
Susana Barros
Svend-Marian Bauer
Mathilde Beaulieu
Olga Bellido-Tirado
Björn Benneke
Thomas Bensby
Edwin Bergin
P. Berio
Katia Biazzo
Laurent Bigot
Arjan Bik
Jayne L. Birkby
Nicolas Blind
Olivier Boebion
Isabelle Boisse
Emeline Bolmont
J. S. Bolton
Marco Bonaglia
Xavier Bonfils
Lea Bonhomme
Francesco Borsa
Jean-Claude Bouret
Alexis Brandeker
Wolfgang Brandner
Christopher H. Broeg
Matteo Brogi
Denis Brousseau
Anna Brucalassi
Joar G. Brynnel
Lars A. Buchhave
David F. Buscher
Lorenzo Cabona
A. Cabral
Alexandre Cabral
Giorgio Calderone
Rocío Calvo-Ortega
Faustine Cantalloube
Bruno L. Canto Martins
Luca Carbonaro
Yan Caujolle
Gaël Chauvin
Bruno Chazelas
Anne-Laure L. Cheffot
Yuk Shan Cheng
Andrea Chiavassa
Lise B. Christensen
Roberto Cirami
Michele Cirasuolo
Neil J. Cook
Ryan Cooke
Igor Coretti
Stefano Covino
Nicolas B. Cowan
Giovanni Cresci
Stefano Cristiani
Vanderlei Cunha Parro
Guido Cupani
Valentina D'Odorico
Kamalesh Dadi
Izan C. de Castro Leão
Annalisa De Cia
Jose R. De Medeiros
Florian Debras
Michael Debus
Alain Delorme
Olivier Demangeon
Frederic Derie
M. Dessauges-Zavadsky
Paolo Di Marcantonio
Simona Di Stefano
Frank Dionies
Armando Domiciano de Souza
René Doyon
Jennifer S. Dunn
Sébastien E. Egner
David Ehrenreich
Joao P. Faria
Debora Ferruzzi
Chiara Feruglio
Martin Fisher
Adriano Fontana
B S. Frank
C. Fuesslein
M. Fumagalli
Thierry Fusco
Johan P. U. Fynbo
O. Gabella
W. Gaessler
E. Gallo
X. Gao
L. Genolet
M. Genoni
P. Giacobbe
E. Giro
R. S. Gonçalves
O. A. Gonzalez
J. I. González-Hernández
C. Gouvret
F. Gracia Témich
M. G. Haehnelt
C. Haniff
A. Hatzes
R. Helled
H. J. Hoeijmakers
I. Hughes
Philipp Huke
Y. Ivanisenko
A. S. Järvinen
S. P. Järvinen
A. Kaminski
J. Kern
J. Knoche
A. Kordt
H. Korhonen
A. Korn
D. Kouach
G. Kowzan
L. Kreidberg
M. Landoni
A. A. Lanotte
A. Lavail
B. Lavie
D. Lee
M. Lehmitz
Jian Li
Wei Li
J. Liske
C. Lovis
S. Lucatello
D. Lunney
M. J. MacIntosh
N. Madhusudhan
L. Magrini
R. Maiolino
J. Maldonado
L. Malo
A. W. S. Man
T. Marquart
C. M. J. Marques
E. L. Marques
P. Martinez
A. M. Martins
C. J. A. P. Martins
J. H. C. Martins
P. Maslowski
C. Mason
E. Mason
R. A. McCracken
M. A. F. Melo e Sousa
P. Mergo
G. Micela
D. Milaković
P. Mollière
M. A. Monteiro
D. Montgomery
C. Mordasini
J. Morin
A. Mucciarelli
M. T. Murphy
M. N'Diaye
N. Nardetto
B. Neichel
N. Neri
A. T. Niedzielski
E. Niemczura
B. Nisini
L. Nortmann
P. Noterdaeme
N. J. Nunes
L. Oggioni
F. Olchewsky
E. Oliva
H. Önel
L. Origlia
G. Östlin
N. N.-Q. Ouellette
Enric Pallé
P. Papaderos
G. Pariani
L. Pasquini
J. Peñate Castro
F. Pepe
C. Peroux
L. Perreault Levasseur
Sandrine Perruchot
P. Petit
Oliver Pfuhl
L. Pino
Javier Piqueras
N. Piskunov
A. Pollo
K. Poppenhaeger
M. Porru
J. Puschnig
A. Quirrenbach
Emily Rauscher
R. Rebolo
E. M. A. Redaelli
S. Reffert
D. T. Reid
A. Reiners
P. Richter
M. Riva
S. Rivoire
C. Rodríguez-López
I. U. Roederer
D. Romano
M. Roth
S. Rousseau
J. Rowe
A. Saccardi
S. Salvadori
N. Sanna
N. C. Santos
P. Santos Diaz
Jorge Sanz-Forcada
M. Sarajlic
J.-F. Sauvage
D. Savio
A. Scaudo
S. Schäfer
R. P. Schiavon
T. M. Schmidt
C. Selmi
R. Simoes
A. Simonnin
S. Sivanandam
M. Sordet
R. Sordo
F. Sortino
D. Sosnowska
S. G. Sousa
A. Spang
R. Spiga
E. Stempels
J. R. Y. Stevenson
Klaus G. Strassmeier
A. Suárez Mascareño
A. Sulich
X. Sun
N. R. Tanvir
F. Tenegi-Sanginés
S. Thibault
S. J. Thompson
P. Tisserand
A. Tozzi
M. Turbet
J.-P. Véran
Julien Veran
P. Vallée
I. Vanni
R. Varas
A. Vega-Moreno
K. A. Venn
A. Verma
J. Vernet
M. Viel
G. Wade
C. Waring
M. Weber
J. Weder
B. Wehbé
J. Weingrill
M. Woche
M. Xompero
E. Zackrisson
A. Zanutta
M. R. Zapatero Osorio
M. Zechmeister
J. Zimara
2024-07-17
Ground-based and Airborne Instrumentation for Astronomy X (publié)
doi.org
arxiv.org
Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review (Preprint)
Raymond Tolentino
Ashkan Baradaran
Genevieve Gore
Pierre Pluye
Samira Abbasgholizadeh-Rahimi
BACKGROUND

The successful integration of artificial intelligence (AI) in… (voir plus)to clinical practice is contingent upon physicians’ comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process.

 OBJECTIVE

The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.

 METHODS

We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.

 RESULTS

Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.

 CONCLUSIONS

This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs.

 INTERNATIONAL REGISTERED REPORT
2024-07-17
JMIR Medical Education (publié)
doi.org
Myelin basic protein mRNA levels affect myelin sheath dimensions, architecture, plasticity, and density of resident glial cells
Hooman Bagheri
Hana Friedman
Amanda Hadwen
Celia Jarweh
Ellis Cooper
Lawrence Oprea
Claire Guerrier
Anmar Khadra
Armand Collin
Julien Cohen‐Adad
Amanda Young
Gerardo Mendez Victoriano
Matthew Swire
Andrew Jarjour
Marie E. Bechler
Rachel S. Pryce
Pierre Chaurand
Lise Cougnaud
Dajana Vuckovic
Elliott Wilion … (voir 11 de plus)
Owen Greene
Akiko Nishiyama
Anouk Benmamar‐Badel
Trevor Owens
Vladimir Grouza
Marius Tuznik
Hanwen Liu
David A. Rudko
Jinyi Zhang
Katherine A. Siminovitch
Alan C. Peterson
Myelin Basic Protein (MBP) is essential for both elaboration and maintenance of CNS myelin, and its reduced accumulation results in hypomyel… (voir plus)ination. How different Mbp mRNA levels affect myelin dimensions across the lifespan and how resident glial cells may respond to such changes are unknown. Here, to investigate these questions, we used enhancer‐edited mouse lines that accumulate Mbp mRNA levels ranging from 8% to 160% of wild type. In young mice, reduced Mbp mRNA levels resulted in corresponding decreases in Mbp protein accumulation and myelin sheath thickness, confirming the previously demonstrated rate‐limiting role of Mbp transcription in the control of initial myelin synthesis. However, despite maintaining lower line specific Mbp mRNA levels into old age, both MBP protein levels and myelin thickness improved or fully normalized at rates defined by the relative Mbp mRNA level. Sheath length, in contrast, was affected only when mRNA levels were very low, demonstrating that sheath thickness and length are not equally coupled to Mbp mRNA level. Striking abnormalities in sheath structure also emerged with reduced mRNA levels. Unexpectedly, an increase in the density of all glial cell types arose in response to reduced Mbp mRNA levels. This investigation extends understanding of the role MBP plays in myelin sheath elaboration, architecture, and plasticity across the mouse lifespan and illuminates a novel axis of glial cell crosstalk.
2024-07-17
Glia (publié)
doi.org
The Madness of Multiple Entries in March Madness
Jeff Decary
David Bergman
Carlos Henrique Cardonha
Jason Imbrogno
Andrea Lodi
This paper explores multi-entry strategies for betting pools related to single-elimination tournaments. In such betting pools, participants … (voir plus)select winners of games, and their respective score is a weighted sum of the number of correct selections. Most betting pools have a top-heavy payoff structure, so the paper focuses on strategies that maximize the expected score of the best-performing entry. There is no known closed-formula expression for the estimation of this metric, so the paper investigates the challenges associated with the estimation and the optimization of multi-entry solutions. We present an exact dynamic programming approach for calculating the maximum expected score of any given fixed solution, which is exponential in the number of entries. We explore the structural properties of the problem to develop several solution techniques. In particular, by extracting insights from the solutions produced by one of our algorithms, we design a simple yet effective problem-specific heuristic that was the best-performing technique in our experiments, which were based on real-world data extracted from recent March Madness tournaments. In particular, our results show that the best 100-entry solution identified by our heuristic had a 2.2% likelihood of winning a
2024-07-17
ArXiv (prépublication)
doi.org
arxiv.org
Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
Ziyan Luo
Xujie Si
Solving Constrained Horn Clauses (CHCs) is a fundamental challenge behind a wide range of verification and analysis tasks. Data-driven appro… (voir plus)aches show great promise in improving CHC solving without the painstaking manual effort of creating and tuning various heuristics. However, a large performance gap exists between data-driven CHC solvers and symbolic reasoning-based solvers. In this work, we develop a simple but effective framework,"Chronosymbolic Learning", which unifies symbolic information and numerical data points to solve a CHC system efficiently. We also present a simple instance of Chronosymbolic Learning with a data-driven learner and a BMC-styled reasoner. Despite its great simplicity, experimental results show the efficacy and robustness of our tool. It outperforms state-of-the-art CHC solvers on a dataset consisting of 288 benchmarks, including many instances with non-linear integer arithmetics.
2024-07-16
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org