Publications

Is Meta-training Really Necessary for Molecular Few-Shot Learning ?
Philippe Formont
Hugo Jeannin
Pablo Piantanida
Ismail Ben Ayed
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving con… (see more)voluted meta-learning strategies. We revisit the more straightforward fine-tuning approach for molecular data, and propose a regularized quadratic-probe loss based on the the Mahalanobis distance. We design a dedicated block-coordinate descent optimizer, which avoid the degenerate solutions of our loss. Interestingly, our simple fine-tuning approach achieves highly competitive performances in comparison to state-of-the-art methods, while being applicable to black-box settings and removing the need for specific episodic pre-training strategies. Furthermore, we introduce a new benchmark to assess the robustness of the competing methods to domain shifts. In this setting, our fine-tuning baseline obtains consistently better results than meta-learning methods.
2024-04-01
ArXiv (preprint)
doi.org
arxiv.org
Acheiving United Nations' SDG3 Through Empowering Health Artificial Intelligence on Resource-Constrained Mobile Devices Without Connectivity
Tianyi Yang
Tianze Yang
Shaoshan Liu
Xue Liu
At least half of the world's population do not have access to essential health services. Worse, large numbers of households are being pushed… (see more) into poverty because they must pay for health care out of their own pockets.
2024-03-31
SIGCAS Comput. Soc. (published)
doi.org
Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy
Abdul Al-Shawwa
Kalum Ost
David C. Anderson
Newton Cho
Nathan Evaniew
W. Bradley Jacobs
Allan R. Martin
Ranjeet Gaekwad
Saswati Tripathy
Jacques Bouchard
Steve Casha
Roger Cho
S. Duplessis
Peter Lewkonia
Fred Nicholls
Paul Salo
Alex Soroceanu
Ganesh Swamy
Kenneth Thomas
Michael Yang … (see 2 more)
Julien Cohen‐Adad
David W. Cadotte
2024-03-31
The Spine Journal (published)
doi.org
Co-developing longitudinal patient registries for phenylketonuria and mucopolysaccharidoses in Canada
John Adams
Kim Angel
John J. Mitchell
Pranesh Chakraborty
Beth K. Potter
Michal Inbar-Feigenberg
Sylvia Stockler
Monica Lamoureux
Alison H. Howie
Alex Pace
Nancy J. Butcher
Cheryl Rockman-Greenberg
Robin Hayeems
Anne-Marie Laberge
Thierry Lacaze-Masmonteil
Jeff Round
Martin Offringa
Maryam Oksoui
Andreas Schulze
Kathy N. Speechley … (see 3 more)
Kednapa Thavorn
Yannis Trakadis
Kumanan Wilson
2024-03-31
Molecular Genetics and Metabolism (published)
doi.org
Deployment of digital technologies in African cities: emerging issues and policy recommendations for local governments
Leandry Jieutsa
Irina Gbaguidi
Wijdane Nadifi
Shin Koseki
2024-03-31
Data & Policy (published)
doi.org
Increasing schedule reliability in the multiple depot vehicle scheduling problem with stochastic travel time
L'ea Ricard
Guy Desaulniers
Andrea Lodi
Louis-Martin Rousseau
2024-03-31
Omega (published)
doi.org
Machine Learning Robustness: A Primer
Houssem Ben Braiek
Foutse Khomh
This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness … (see more)in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.
2024-03-31
ArXiv (preprint)
doi.org
arxiv.org
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Aleksandar Botev
Soham De
Samuel L. Smith
Anushan Fernando
George-Cristian Muraru
Ruba Haroun
Leonard Berrada
Razvan Pascanu
Pier Giuseppe Sessa
Robert Dadashi
L'eonard Hussenot
Johan Ferret
Sertan Girgin
Olivier Bachem
Alek Andreev
Kathleen Kenealy
Thomas Mesnard
Cassidy Hardin
Surya Bhupatiraju
Shreya Pathak … (see 43 more)
Laurent Sifre
Morgane Rivière
Mihir Kale
J Christopher Love
Juliette Love
Pouya Dehghani Tafti
Armand Joulin
Noah Fiedel
Evan Senter
Yutian Chen 0001
Srivatsan Srinivasan
Guillaume Desjardins
David Mark Budden
Arnaud Doucet
Sharad Mandyam Vikram
Adam Paszke
Trevor Gale
Sebastian Borgeaud
Charlie Chen
Andy Brock
Antonia Paterson
Jenny Brennan
Meg Risdal
Raj Gundluru
N. Devanathan
Paul Mooney
Nilay Chauhan
Phil Culliton
Luiz GUStavo Martins
Elisa Bandy
David W. Huntsperger
Glenn Cameron
Arthur Zucker
Tris Brian Warkentin
Ludovic Peran
Minh Giang
Zoubin Ghahramani
Clément Farabet
Koray Kavukcuoglu
Demis Hassabis
Raia Hadsell
Yee Whye Teh
Nando de Frietas
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurr… (see more)ences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide two sizes of models, containing 2B and 9B parameters, and provide pre-trained and instruction tuned variants for both. Our models achieve comparable performance to similarly-sized Gemma baselines despite being trained on fewer tokens.
2024-03-31
arXiv (published)
doi.org
arxiv.org
Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots
Chao Shen
Wenkang Zhan
Kaiyao Xin
Manyang Li
Zhenyu Sun
Hui Cong
Chi Xu
Jian Tang
Zhaofeng Wu
Bo Xu
Zhongming Wei
Chunlai Xue
Chao Zhao
Zhanguo Wang
2024-03-28
Nature Communications (published)
doi.org
arxiv.org
Scaling up ridge regression for brain encoding in a massive individual fMRI dataset
Sana Ahmadi
Lune P Bellec
Tristan Glatard
2024-03-27
ArXiv (preprint)
doi.org
arxiv.org
Fast burst fraction transients convey information independent of the firing rate
Richard Naud
Xingyun Wang
Zachary Friedenberger
Alexandre Payeur
Jiyun N Shin
Jean-Claude Beique
Blake Richards
Moritz Drüke
Matthew Larkum
Guy Doron
Theories of attention and learning have hypothesized a central role for high-frequency bursting in cognitive functions, but experimental rep… (see more)orts of burst-mediated representations \emph{in vivo} have been limited. Here we used a novel demultiplexing approach by considering a conjunctive burst code. We studied this code \emph{in vivo} while animals learned to report direct electrical stimulation of the somatosensory cortex and found two acquired yet independent representations. One code, the event rate, showed a sparse and succint stiumulus representation and a small modulation upon detection errors. The other code, the burst fraction, correlated more globally with stimulation and more promptly responded to detection errors. Potent and fast modulations of the burst fraction were seen even in cells that were considered unresponsive based on the firing rate. During the later stages of training, this modulation in bursting happened earlier, gradually aligning temporally with the representation in event rate. The alignment of bursting and event rate modulation sharpened the firing rate response, and was strongly associated with behavioral accuracy. Thus a fine-grained separation of spike timing patterns reveals two signals that accompany stimulus representations: an error signal that can be essential to guide learning and a sharpening signal that could implement attention mechanisms.
2024-03-26
bioRxiv (preprint)
doi.org
Predicting Species Occurrence Patterns from Partial Observations
Hager Radi
Mélisande Teng
David Rolnick
To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are chang… (see more)ing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.
2024-03-25
ArXiv (preprint)
doi.org
arxiv.org