Publications

Aftereffects following adaptation to face mental images
Mathias Salvas-Hébert
Vincent Taschereau-Dumouchel
Ian Charest
Frédéric Gosselin
2024-09-14
Journal of Vision (publié)
doi.org
Exploiting large-scale neuroimaging datasets to reveal novel insights in vision science
Ian Charest
Peter Brotherwood
Catherine Landry
Jasper van den Bosch
Shahab Bakhtiari
Tim Kietzmann
Frédéric Gosselin
Adrien Doerig
2024-09-14
Journal of Vision (publié)
doi.org
GFlowNet Pretraining with Inexpensive Rewards
Mohit Pandey
Gopeshh Subbaraj
Emmanuel Bengio
Generative Flow Networks (GFlowNets), a class of generative models have recently emerged as a suitable framework for generating diverse and … (voir plus)high-quality molecular structures by learning from unnormalized reward distributions. Previous works in this direction often restrict exploration by using predefined molecular fragments as building blocks, limiting the chemical space that can be accessed. In this work, we introduce Atomic GFlowNets (A-GFNs), a foundational generative model leveraging individual atoms as building blocks to explore drug-like chemical space more comprehensively. We propose an unsupervised pre-training approach using offline drug-like molecule datasets, which conditions A-GFNs on inexpensive yet informative molecular descriptors such as drug-likeliness, topological polar surface area, and synthetic accessibility scores. These properties serve as proxy rewards, guiding A-GFNs towards regions of chemical space that exhibit desirable pharmacological properties. We further our method by implementing a goal-conditioned fine-tuning process, which adapts A-GFNs to optimize for specific target properties. In this work, we pretrain A-GFN on the ZINC15 offline dataset and employ robust evaluation metrics to show the effectiveness of our approach when compared to other relevant baseline methods in drug design.
2024-09-14
ArXiv (prépublication)
doi.org
arxiv.org
Neural responses in space and time to a massive set of natural scenes
Peter Brotherwood
Emmanuel Lebeau
Mathias Salvas-Hébert
Marin Coignard
Shahab Bakhtiari
Frédéric Gosselin
Kendrick Kay
Ian Charest
2024-09-14
Journal of Vision (publié)
doi.org
Swarming Out of the Lab: Comparing Relative Localization Methods for Collective Behavior
Rafael Gomes Braga
Vivek Shankar Vardharajan
Giovanni Beltrame
David St-Onge
2024-09-14
Lecture Notes in Computer Science (publié)
doi.org
Unveiling Mental Imagery: Enhanced Mental Images Reconstruction using EEG and the Bubbles Method
Audrey Lamy-Proulx
Laurence Leblond
Jasper van den Bosch
Catherine Landry
Peter Brotherwood
Vincent Taschereau-Dumouchel
Frédéric Gosselin
Ian Charest
2024-09-14
Journal of Vision (publié)
doi.org
When Machines Outshine Humans in Object Recognition, Benchmarking Dilemma
Mohammad Javad Darvishi Bayazi
Md Rifat Arefin
Jocelyn Faubert
Irina Rish
2024-09-14
Journal of Vision (publié)
doi.org
A high-throughput phenotypic screen combined with an ultra-large-scale deep learning-based virtual screening reveals novel scaffolds of antibacterial compounds
Gabriele Scalia
Steven T. Rutherford
Ziqing Lu
Kerry R. Buchholz
Nicholas Skelton
Kangway Chuang
Nathaniel Diamant
Jan-Christian Hütter
Jerome-Maxim Luescher
Anh Miu
Jeff Blaney
Leo Gendelev
Elizabeth Skippington
Greg Zynda
Nia Dickson
Michał Koziarski
Yoshua Bengio
Aviv Regev
Man-Wah Tan
Tommaso Biancalani
The proliferation of multi-drug-resistant bacteria underscores an urgent need for novel antibiotics. Traditional discovery methods face chal… (voir plus)lenges due to limited chemical diversity, high costs, and difficulties in identifying structurally novel compounds. Here, we explore the integration of small molecule high-throughput screening with a deep learning-based virtual screening approach to uncover new antibacterial compounds. Leveraging a diverse library of nearly 2 million small molecules, we conducted comprehensive phenotypic screening against a sensitized Escherichia coli strain that, at a low hit rate, yielded thousands of hits. We trained a deep learning model, GNEprop, to predict antibacterial activity, ensuring robustness through out-of-distribution generalization techniques. Virtual screening of over 1.4 billion compounds identified potential candidates, of which 82 exhibited antibacterial activity, illustrating a 90X improved hit rate over the high-throughput screening experiment GNEprop was trained on. Importantly, a significant portion of these newly identified compounds exhibited high dissimilarity to known antibiotics, indicating promising avenues for further exploration in antibiotic discovery.
2024-09-13
bioRxiv (prépublication)
doi.org
Trimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems
Altaf Allah Abbassi
Houssem Ben Braiek
Foutse Khomh
Thomas Reid
2024-09-12
ArXiv (prépublication)
doi.org
arxiv.org
End-to-end Conditional Robust Optimization
Abhilash Reddy Chenreddy
Erick Delage
2024-09-11
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence (publié)
proceedings.mlr.press
proceedings.mlr.press
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models
Lucas Berry
Axel Brando
David Meger
Generative diffusion models, notable for their large parameter count (exceeding 100 million) and operation within high-dimensional image spa… (voir plus)ces, pose significant challenges for traditional uncertainty estimation methods due to computational demands. In this work, we introduce an innovative framework, Diffusion Ensembles for Capturing Uncertainty (DECU), designed for estimating epistemic uncertainty for diffusion models. The DECU framework introduces a novel method that efficiently trains ensembles of conditional diffusion models by incorporating a static set of pre-trained parameters, drastically reducing the computational burden and the number of parameters that require training. Additionally, DECU employs Pairwise-Distance Estimators (PaiDEs) to accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The effectiveness of this framework is demonstrated through experiments on the ImageNet dataset, highlighting its capability to capture epistemic uncertainty, specifically in under-sampled image classes.
2024-09-11
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence (publié)
doi.org
proceedings.mlr.press
Feedback-guided Data Synthesis for Imbalanced Classification
Reyhane Askari Hemmat
Mohammad Pezeshki
Florian Bordes
Michal Drozdzal
Adriana Romero
Current status quo in machine learning is to use static datasets of real images for training, which often come from long-tailed distribution… (voir plus)s. With the recent advances in generative models, researchers have started augmenting these static datasets with synthetic data, reporting moderate performance improvements on classification tasks. We hypothesize that these performance gains are limited by the lack of feedback from the classifier to the generative model, which would promote the usefulness of the generated samples to improve the classifier's performance. In this work, we introduce a framework for augmenting static datasets with useful synthetic samples, which leverages one-shot feedback from the classifier to drive the sampling of the generative model. In order for the framework to be effective, we find that the samples must be close to the support of the real data of the task at hand, and be sufficiently diverse. We validate three feedback criteria on a long-tailed dataset (ImageNet-LT, Places-LT) as well as a group-imbalanced dataset (NICO++). On ImageNet-LT, we achieve state-of-the-art results, with over
2024-09-10
TMLR (accepté)
doi.org
openreview.net