Publications

Impact of through‐slice gradient optimization for dynamic slice‐wise shimming in the cervico‐thoracic spinal cord
Arnaud Breheret
Alexandre D'Astous
Yixin Ma
Jason P. Stockmann
Julien Cohen‐Adad
This study investigates the effectiveness of through‐slice gradient optimization in dynamic slice‐wise B0 shimming of the cervico‐thor… (see more)acic spinal cord to enhance signal recovery in gradient‐echo (GRE) EPI sequences commonly used in functional MRI studies. Six volunteers underwent MRI acquisitions with dynamic shim updating (DSU) using a custom‐built 15‐channel AC/DC coil at 3 T. A magnetization‐prepared rapid gradient echo was acquired to segment the spine and to provide a clear image of the anatomical region of interest in the figures. GRE B0 field maps were used to measure field homogeneity before and after shimming; the pre‐shimming field map was used for optimization. Shimmed fields were dynamically applied to GRE–echo planar imaging acquisitions simulating functional MRI acquisitions under two shimming conditions: DSU with and without through‐slice gradient consideration. DSU with through‐slice gradient optimization increased the temporal signal‐to‐noise ratio at the T2 vertebral level by 201% compared with volume‐wise shim and by 28% compared with DSU without through‐slice. The residual geometric distortions were similar between DSU with and without through‐slice gradient optimization. A high signal loss penalty parameter was effective in simulations for reducing through‐slice gradient‐induced signal loss but led to instability and reduced image quality in actual acquisitions due to excessive in‐plane B0 inhomogeneities. Introducing a carefully balanced through‐slice gradient parameter in slice‐wise shimming substantially improves signal recovery in axial GRE images of the spinal cord, without compromising in‐plane homogeneity. This effective approach can advance spinal cord functional MRI applications at high field strengths.
2025-04-30
Magnetic Resonance in Medicine (published)
doi.org
Improving the Scaling Laws of Synthetic Data with Deliberate Practice
Reyhane Askari-Hemmat
Mohammad Pezeshki
Elvis Dohmatob
Florian Bordes
Pietro Astolfi
Melissa Hall
Jakob Verbeek
Michal Drozdzal
Adriana Romero-Soriano
Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a nov… (see more)el framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling, enabling models to focus on the most informative synthetic samples. Rather than generating a large dataset and pruning it afterward, DP efficiently approximates the direct generation of informative samples. We theoretically show how training on challenging, informative examples improves scaling laws and empirically validate that DP achieves better scaling performance with significantly fewer training samples and iterations. On ImageNet-100, DP generates 3.4x fewer samples and requires six times fewer iterations, while on ImageNet-1k, it generates 8x fewer samples with a 30 percent reduction in iterations, all while achieving superior performance compared to prior work.
2025-04-30
ICML.cc/2025/Conference (oral)
doi.org
proceedings.mlr.press
Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
Sergio Arnaud
Paul McVay
Ada Martin
Arjun Majumdar
Krishna Murthy
Phillip Thomas
Ruslan Partsey
Daniel Dugas
Abha Gejji
Alexander Sax
Vincent-Pierre Berges
Mikael Henaff
Ayush Jain
Ang Cao
Ishita Prasad
Mrinal Kalakrishnan
Michael G. Rabbat
Nicolas Ballas
Mahmoud Assran
Oleksandr Maksymets … (see 2 more)
Aravind Rajeswaran
Franziska Meier
2025-04-30
ICML.cc/2025/Conference (poster)
doi.org
proceedings.mlr.press
Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization
Phillip Huang Guo
Aaquib Syed
Abhay Sheshadri
Aidan Ewart
Gintare Karolina Dziugaite
2025-04-30
ICML.cc/2025/Conference (poster)
doi.org
proceedings.mlr.press
Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
Hongyao Tang
Johan Obando-Ceron
Pablo Samuel Castro
Aaron Courville
Glen Berseth
Plasticity, or the ability of an agent to adapt to new tasks, environments, or distributions, is crucial for continual learning. In this pap… (see more)er, we study the loss of plasticity in deep continual RL from the lens of churn: network output variability for out-of-batch data induced by mini-batch training. We demonstrate that (1) the loss of plasticity is accompanied by the exacerbation of churn due to the gradual rank decrease of the Neural Tangent Kernel (NTK) matrix; (2) reducing churn helps prevent rank collapse and adjusts the step size of regular RL gradients adaptively. Moreover, we introduce Continual Churn Approximated Reduction (C-CHAIN) and demonstrate it improves learning performance and outperforms baselines in a diverse range of continual learning environments on OpenAI Gym Control, ProcGen, DeepMind Control Suite, and MinAtar benchmarks.
2025-04-30
International Conference on Machine Learning (poster)
doi.org
proceedings.mlr.press
Multi-Modal Language Models as Text-to-Image Model Evaluators
Jiahui Chen
Candace Ross
Reyhane Askari Hemmat
Koustuv Sinha
Melissa Hall
Michal Drozdzal
Adriana Romero
2025-04-30
ArXiv (preprint)
doi.org
arxiv.org
Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
Guozheng Ma
Lu Li
Zilin Wang
Li Shen
Pierre-Luc Bacon
Dacheng Tao
Effectively scaling up deep reinforcement learning models has proven notoriously difficult due to network pathologies during training, motiv… (see more)ating various targeted interventions such as periodic reset and architectural advances such as layer normalization. Instead of pursuing more complex modifications, we show that introducing static network sparsity alone can unlock further scaling potential beyond their dense counterparts with state-of-the-art architectures. This is achieved through simple one-shot random pruning, where a predetermined percentage of network weights are randomly removed once before training. Our analysis reveals that, in contrast to naively scaling up dense DRL networks, such sparse networks achieve both higher parameter efficiency for network expressivity and stronger resistance to optimization challenges like plasticity loss and gradient interference. We further extend our evaluation to visual and streaming RL scenarios, demonstrating the consistent benefits of network sparsity.
2025-04-30
ICML.cc/2025/Conference (oral)
doi.org
proceedings.mlr.press
Outsourced Diffusion Sampling: Efficient Posterior Inference in Latent Spaces of Generative Models
Siddarth Venkatraman
Mohsin Hasan
Minsu Kim
Luca Scimeca
Marcin Sendera
Yoshua Bengio
Glen Berseth
Nikolay Malkin
Any well-behaved generative model over a variable …
2025-04-30
International Conference on Machine Learning (poster)
doi.org
proceedings.mlr.press
Real-time fine finger motion decoding for transradial amputees with surface electromyography
Zihan Weng
Yang Xiao
Peiyang Li
Chanlin Yi
Pouya Bashivan
Hailin Ma
Guang Yao
Yuan Lin
Fali Li
Dezhong Yao 0001
Jingming Hou
Yangsong Zhang
Peng Xu
2025-04-30
Neural Networks (published)
doi.org
Replication of a GWAS signal near HLA-DQA2 with AML using a disease-only cohort and external population-based controls
Rose Laflamme
Véronique Lisi
Josée Hébert
Guy Sauvageau
Sébastien Lemieux
Vincent-Philippe Lavallee
Guillaume Lettre
2025-04-30
Blood Neoplasia (published)
doi.org
Self-Play Q-Learners Can Provably Collude in the Iterated Prisoner's Dilemma
Quentin Bertrand
Juan Agustin Duque
Emilio Calvano
Gauthier Gidel
A growing body of computational studies shows that simple machine learning agents converge to cooperative behaviors in social dilemmas, such… (see more) as collusive price-setting in oligopoly markets, raising questions about what drives this outcome. In this work, we provide theoretical foundations for this phenomenon in the context of self-play multi-agent Q-learners in the iterated prisoner’s dilemma. We characterize broad conditions under which such agents provably learn the cooperative Pavlov (win-stay, lose-shift) policy rather than the Pareto-dominated “always defect” policy. We validate our theoretical results through additional experiments, demonstrating their robustness across a broader class of deep learning algorithms.
2025-04-30
ICML.cc/2025/Conference (poster)
doi.org
proceedings.mlr.press
On the generalization of language models from in-context learning and finetuning: a controlled study
Andrew Lampinen
Arslan Chaudhry
Stephanie C.Y. Chan
Cody Wild
Diane Wan
Alexander Y. Ku
Alex Ku
Jörg Bornschein
Razvan Pascanu
Murray P. Shanahan
James L McClelland
2025-04-30
ArXiv (preprint)
doi.org
arxiv.org