Publications

Instant3dit: Multiview Inpainting for Fast Editing of 3D Objects
Amir Barda
Matheus Gadelha
Vladimir Kim
Noam Aigerman
Amit H. Bermano
Thibault Groueix
We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in approximately 3 seconds, without t… (voir plus)he need for running an SDS type of optimization. Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be mapped back to any 3D representation using the bank of available Large Reconstruction Models. We explore different fine-tuning strategies to obtain both multiview generation and inpainting capabilities within the same diffusion model. In particular, the design of the inpainting mask is an important factor of training an inpainting model, and we propose several masking strategies to mimic the types of edits a user would perform on a 3D shape. Our approach takes 3D generative editing from hours to seconds and produces higher-quality results compared to previous works.
2025-01-01
CVPR (publié)
doi.org
arxiv.org
Integer Programming Games.
Margarida Carvalho
Gabriele Dragotto
Andrea Lodi 0001
Sriram Sankaranarayanan 0002
2025-01-01
Found. Trends Optim. (publié)
doi.org
Integer Programming Games.
Margarida Carvalho
Gabriele Dragotto
Andrea Lodi
Sriram Sankaranarayanan 0002
2025-01-01
Foundations and Trends® in Optimization (publié)
doi.org
Integrating Generative and Experimental Platforms for Biomolecular Design
Cheng-Hao Liu
Jarrid Rector-Brooks
Soojung Yang
Sidney L Lisanza
Francesca-Zhoufan Li
Hannes Stärk
Jacob Gershon
Lauren Hong
Pranam Chatterjee
Tommi Jaakkola
Regina Barzilay
David Baker
Frances H. Arnold
Yoshua Bengio
Biomolecular design, through artificial engineering of proteins, ligands, and nucleic acids, holds immense promise in addressing pressing me… (voir plus)dical, industrial, and environmental challenges. While generative machine learning has shown significant potential in this area, a palpable disconnect exists with experimental biology: many ML research efforts prioritize static benchmark performance, potentially sidelining impactful biological applications. This workshop seeks to bridge this gap by bringing computationalists and experimentalists together, catalyzing a deeper interdisciplinary discourse. Together, we will explore the strengths and challenges of generative ML in biology, experimental integration of generative ML, and biological problems ready for ML. To attract high-quality and diverse research, we partnered with Nature Biotechnology for a special collection, and we created dedicated tracks for in-silico ML research and hybrid ML-experimental biology research. Our lineup features emerging leaders as speakers and renowned scientists as panelists, encapsulating a spectrum from high-throughput experimentation and computational biology to generative ML. With a diverse organizing team and backed by industry sponsors, we dedicate the workshop to pushing the boundaries of ML's role in biology.
2025-01-01
ICLR.cc/2025/Workshop_Proposals (publié)
openreview.net
openreview.net
Investigating Generalization Behaviours of Generative Flow Networks
Lazar Atanackovic
Emmanuel Bengio
Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spa… (voir plus)ces. Since their inception, GFlowNets have proven to be useful for learning generative models in applications where the majority of the discrete space is unvisited during training. This has inspired some to hypothesize that GFlowNets, when paired with deep neural networks (DNNs), have favourable generalization properties. In this work, we empirically verify some of the hypothesized mechanisms of generalization of GFlowNets. In particular, we find that the functions that GFlowNets learn to approximate have an implicit underlying structure which facilitate generalization. We also find that GFlowNets are sensitive to being trained offline and off-policy; however, the reward implicitly learned by GFlowNets is robust to changes in the training distribution.
2025-01-01
Trans. Mach. Learn. Res. (publié)
doi.org
arxiv.org
Investigating the Effect of Providing Required Training to Mothers of Children with Surgery and Its Effect on Mothers' Anxiety
Julia Ferreira
Nadia Safa
Fabio Botelho
Robin Petroze
Hussein Wissanji
Dan Poenaru
Pramod Puligandla
Kenneth Shaw
Maeve Trudeau
Elena Guadagno
Jean Martin Laberge
Sherif Emil
2025-01-01
Journal of Integrative Nursing and Palliative Care (publié)
doi.org
Investigating the Effect of Providing Required Training to Mothers of Children with Surgery and Its Effect on Mothers' Anxiety
Julia Ferreira
Nadia Safa
Fabio Botelho
Robin Petroze
Hussein Wissanji
Dan Poenaru
Pramod Puligandla
Kenneth Shaw
Maeve Trudeau
Elena Guadagno
Jean-Martin Laberge
Sherif Emil
2025-01-01
Journal of Integrative Nursing and Palliative Care (publié)
doi.org
Learning active tactile perception through belief-space control
Jean-François Tremblay
Johanna Hansen
David Meger
Francois Hogan
Gregory Dudek
Robot operating in an open world can encounter novel objects with unknown physical properties, such as mass, friction, or size. It is desira… (voir plus)ble to be able to sense those property through contact-rich interaction, before performing downstream tasks with the objects. We propose a method for autonomously learning active tactile perception policies, by learning a generative world model leveraging a differentiable bayesian filtering algorithm, and designing an information- gathering model predictive controller. We test the method on three simulated tasks: mass estimation, height estimation and toppling height estimation. Our method is able to discover policies which gather information about the desired property in an intuitive manner.
2025-01-01
ICRA (publié)
doi.org
openreview.net
A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2025-01-01
Trans. Mach. Learn. Res. (publié)
openreview.net
openreview.net
Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging
Amar Kumar
Anita Kriz
Barak Pertzov
Tal Arbel
2025-01-01
CVPR Workshops (publié)
doi.org
arxiv.org
LLMs can learn self-restraint through iterative self-reflection
Alexandre Piché
Aristides Milios
Dzmitry Bahdanau
Chris Pal
2025-01-01
Trans. Mach. Learn. Res. (publié)
doi.org
openreview.net
Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers
Mathieu Boudreau
Agah Karakuzu
Arnaud Boré
Basile Pinsard
Kiril Zelenkovski
Eva Alonso‐Ortiz
Julie Boyle
Lune Bellec
Julien Cohen-Adad
Abstract Quantitative MRI (qMRI) promises better specificity, accuracy, repeatability, and reproducibility relative to its clinically-used q… (voir plus)ualitative MRI counterpart. Longitudinal reproducibility is particularly important in qMRI. The goal is to reliably quantify tissue properties that may be assessed in longitudinal clinical studies throughout disease progression or during treatment. In this work, we present the initial data release of the quantitative MRI portion of the Courtois project on neural modelling (CNeuroMod), where the brain and cervical spinal cord of six participants were scanned at regular intervals over the course of several years. This first release includes 3 years of data collection and up to 10 sessions per participant using quantitative MRI imaging protocols (T1, magnetization transfer (MTR, MTsat), and diffusion). In the brain, T1MP2RAGE, fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) all exhibited high longitudinal reproducibility (intraclass correlation coefficient – ICC ≃ 1 and within-subject coefficient of variations – wCV 1%). The spinal cord cross-sectional area (CSA) computed using T2w images and T1MTsat exhibited the best longitudinal reproducibility (ICC ≃ 1 and 0.7 respectively, and wCV 2.4% and 6.9%). Results from this work show the level of longitudinal reproducibility that can be expected from qMRI protocols in the brain and spinal cord in the absence of hardware and software upgrades, and could help in the design of future longitudinal clinical studies.
2025-01-01
Imaging Neuroscience (publié)
doi.org