Publications

Spinal cord versus brain imaging biomarkers of multiple sclerosis trajectory combining 7T and 3T MRI
Alessandro Miscioscia
Constantina A Treaba
Elena Barbuti
Valeria T Barletta
Jacob A Sloane
Eric C Klawiter
Julien Cohen-Adad
Paolo Gallo
Patrizia Pantano
Caterina Mainero
Abstract In multiple sclerosis, different types of lesions and their localization can have varying effects on clinical disability and diseas… (voir plus)e progression. Ultra-high field 7-Tesla MRI improves the visualization of cortical, especially subpial, lesions and of white matter lesions with a paramagnetic rim that are associated with smoldering inflammation. Spinal cord atrophy is also a critical determinant of clinical disability in multiple sclerosis, but its importance relative to paramagnetic rim and cortical lesions in predicting neurological disability and its progression remains unclear. In this longitudinal study, we aimed to identify the most relevant predictors of both the baseline Expanded Disability Status Scale status and 4-year progression independent of relapse activity in a heterogeneous multiple sclerosis cohort. One-hundred-twelve patients (83 relapsing-remitting and 29 secondary progressive; mean age 42.3 years, mean disease duration 9.8 years) underwent 7-Tesla T2* susceptibility-weighted images to segment paramagnetic rim lesions, non-rim white matter lesions and cortical lesions; 3-Tesla T1-weighted brain MRI images extended to the C2-C3 spinal cord were employed to obtain brain volumes and the spinal cord C2-C3 cross-sectional area using FreeSurfer and Spinal Cord Toolbox. Clinical disability was assessed through the Expanded Disability Status Scale at baseline and, in 97/112 patients (86.6%), after a mean follow-up of 4.0 years. The association between imaging metrics and clinical outcome was evaluated using correlations and regression models, corrected for age, sex, treatment class and clinical follow-up time. The main predictors of baseline Expanded Disability Status Scale were cortical lesion (β = 2.9 × 10−4, P = 0.001), non-rim white matter lesion (β = 1.2 × 10−4, P 0.001) volumes, brain white matter volume (β = −15.68, P = 0.017) and C2-C3 cross-sectional area (β = −0.68, P = 0.003). At follow-up, 23/97 patients (24%) experienced progression independent of relapse activity. Progression independent of relapse activity was associated with paramagnetic rim lesion volume (odds ratio = 1.0006 per mm³ increase, P = 0.030), cortical lesion volume (odds ratio = 1.0005 per mm³ increase, P = 0.011) and brain white matter volume (odds ratio = 0.97 × 10−20, P 0.001). However, a stepwise logistic regression model assessing clinical, lesion and atrophy variables identified cortical lesion volume as the strongest independent predictor of progression independent of relapse activity (odds ratio = 1.0006 per mm³ increase, P = 0.005). In multiple sclerosis, different imaging biomarkers contribute differently to current disability and progression independent of relapse activity. Spinal cord atrophy mainly explains the current Expanded Disability Status Scale, while brain white matter atrophy and paramagnetic rim lesions provide additional insights into future disability trajectory. Among all markers, cortical lesions emerged as the main driver for progression independent of relapse activity.
2025-12-31
Brain Communications (publié)
doi.org
Squeezing More from the Stream : Learning Representation Online for Streaming Reinforcement Learning
Nilaksh
Antoine Clavaud
Mathieu Reymond
Francois Rivest
Sarath Chandar
In streaming Reinforcement Learning (RL), transitions are observed and discarded immediately after a single update. While this minimizes res… (voir plus)ource usage for on-device applications, it makes agents notoriously sample-inefficient, since value-based losses alone struggle to extract meaningful representations from transient data. We propose extending Self-Predictive Representations (SPR) to the streaming pipeline to maximize the utility of every observed frame. However, due to the highly correlated samples induced by the streaming regime, naively applying this auxiliary loss results in training instabilities. Thus, we introduce orthogonal gradient updates relative to the momentum target and resolve gradient conflicts arising from streaming-specific optimizers. Validated across the Atari, MinAtar, and Octax suites, our approach systematically outperforms existing streaming baselines. Latent-space analysis, including t-SNE visualizations and effective-rank measurements, confirms that our method learns significantly richer representations, bridging the performance gap caused by the absence of a replay buffer, while remaining efficient enough to train on just a few CPU cores.
2025-12-31
International Conference on Machine Learning (Accept (regular))
doi.org
openreview.net
SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning
Riyasat Ohib
Bishal Thapaliya
Gintare Karolina Dziugaite
Jingyu Liu 0001
Vince D. Calhoun
Sergey Plis
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient commu… (voir plus)nication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each round between the clients and the server. On standard benchmarks including CIFAR-10, CIFAR-100, and Tiny-ImageNet, SSFL consistently improves the accuracy sparsity trade off, achieving more than 20\% relative error reduction on CIFAR-10 compared to the strongest sparse baseline, while reducing communication costs by
2025-12-31
Trans. Mach. Learn. Res. (publié)
arxiv.org
arxiv.org
Stable Deep Reinforcement Learning via Isotropic Gaussian Representations
Ali Saheb Pasand
Johan Obando-Ceron
Aaron Courville
Pouya Bashivan
Pablo Samuel Castro
Deep reinforcement learning systems often suffer from unstable training dynamics due to non-stationarity, where learning objectives and data… (voir plus) distributions evolve over time. We show that under non-stationary targets, isotropic Gaussian embeddings are provably advantageous. In particular, they induce stable tracking of time-varying targets for linear readouts, achieve maximal entropy under a fixed variance budget, and encourage a balanced use of all representational dimensions--all of which enable agents to be more adaptive and stable. Building on this insight, we propose the use of Sketched Isotropic Gaussian Regularization for shaping representations toward an isotropic Gaussian distribution during training. We demonstrate empirically, over a variety of domains, that this simple and computationally inexpensive method improves performance under non-stationarity while reducing representation collapse, neuron dormancy, and training instability.
2025-12-31
International Conference on Machine Learning (Accept (spotlight))
doi.org
openreview.net
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
Yoshua Bengio
Michael Cohen
Damiano Fornasiere
Joumana Ghosn
Pietro Greiner
Matt MacDermott
Sören Mindermann
Adam Oberman
Jesse Richardson
Oliver Richardson
Marc-Antoine Rondeau
Pierre-Luc St-Charles
David Williams-King
The leading AI companies are increasingly focused on building generalist AI agents -- systems that can autonomously plan, act, and pursue go… (voir plus)als across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by malicious actors to a potentially irreversible loss of human control. We discuss how these risks arise from current AI training methods. Indeed, various scenarios and experiments have demonstrated the possibility of AI agents engaging in deception or pursuing goals that were not specified by human operators and that conflict with human interests, such as self-preservation. Following the precautionary principle, we see a strong need for safer, yet still useful, alternatives to the current agency-driven trajectory. Accordingly, we propose as a core building block for further advances the development of a non-agentic AI system that is trustworthy and safe by design, which we call Scientist AI. This system is designed to explain the world from observations, as opposed to taking actions in it to imitate or please humans. It comprises a world model that generates theories to explain data and a question-answering inference machine. Both components operate with an explicit notion of uncertainty to mitigate the risks of overconfident predictions. In light of these considerations, a Scientist AI could be used to assist human researchers in accelerating scientific progress, including in AI safety. In particular, our system can be employed as a guardrail against AI agents that might be created despite the risks involved. Ultimately, focusing on non-agentic AI may enable the benefits of AI innovation while avoiding the risks associated with the current trajectory. We hope these arguments will motivate researchers, developers, and policymakers to favor this safer path.
2025-12-31
Zenodo (publié)
doi.org
arxiv.org
Supervised Multimodal Model for Plasma Spray Diagnostics and Spray Health Monitoring
Cormac Cureton
Abhijeet Praveen
Sareh Soleimani
Aman Sidhu
Cristian Cojocaru
Kintak Raymond Yu
Narges Armanfard
2025-12-31
SSRN Electronic Journal (accepté)
doi.org
Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization
Yonghan Yang
Ye Yuan
Zipeng Sun
Linfeng Du
Bowei He
Haolun Wu
Can Chen
Xue Liu
Offline black-box optimization aims to discover novel designs with high property scores using only a static dataset, a task fundamentally ch… (voir plus)allenged by the out-of-distribution (OOD) extrapolation problem. Existing approaches typically bifurcate into inverse methods, which struggle with the ill-posed nature of mapping scores to designs, and forward methods, which often lack the distributional expressivity to quantify uncertainty effectively. In this work, we propose \textbf{SPADE} (\textbf{S}upport-\textbf{P}roximity \textbf{A}ugmented \textbf{D}iffusion \textbf{E}stimation), a novel framework that reimagines forward surrogate modeling through the lens of conditional generative modeling. SPADE models the forward likelihood
2025-12-31
International Conference on Machine Learning (Accept (regular))
doi.org
openreview.net
Suspected Biliary Atresia in Brazil: Impact of Regional Healthcare Variations on Diagnostic Timeliness
Luiza Telles
Paulo Henrique Moreira Melo
Ana Maria Bicudo Diniz
Gabriele Lech
Ayla Gerk
Lauren Kratky
Dan Poenaru
David P. Mooney
Joaquim Bustorff-Silva
2025-12-31
Journal of Pediatric Surgery Open (publié)
doi.org
SynCoGen: Synthesizable 3D Molecule Generation via Joint Reaction and Coordinate Modeling
Andrei Rekesh
Miruna Cretu
Dmytro Shevchuk
Vignesh Ram Somnath
Pietro Lio
Robert A. Batey
Mike Tyers
Michał Koziarski
Cheng-Hao Liu
Ensuring synthesizability in generative small molecule design remains a major challenge. While recent developments in synthesizable molecule… (voir plus) generation have demonstrated promising results, these efforts have been largely confined to 2D molecular graph representations, limiting the ability to perform geometry-based conditional generation. In this work, we present SYNCOGEN (Synthesizable Co-Generation), a single framework that combines simultaneous masked graph diffusion and flow matching for synthesizable 3D molecule generation. SYNCOGEN samples from the joint distribution of molecular building blocks, chemical reactions, and atomic coordinates. To train the model, we curated SYNSPACE, a dataset containing over 600K synthesis-aware building block graphs and 3.3M conformers. SYNCOGEN achieves state-of-the-art performance in unconditional small molecule graph and conformer generation, and the model delivers competitive performance in zero-shot molecular linker design and pharmacophore conditioning for protein ligand generation in drug discovery. Overall, this multimodal formulation represents a foundation for future applications enabled by non-autoregressive molecular generation, including analog expansion, lead optimization, and direct structure conditioning.
2025-12-31
International Conference on Learning Representations (Accept (Poster))
doi.org
openreview.net
Tequila: Deadzone-free Ternary Quantization for Large Language Models
Hong Huang
Decheng Wu
Rui Cen
Guanghua Yu
Zonghang Li
Kai Liu
Jianchen Zhu
Peng Chen
Xue Liu
Dapeng Wu
Quantization techniques are essential for the deployment of Large Language Models (LLMs) on edge devices. However, prevailing methods often … (voir plus)rely on mixed-precision multiplication that lacks efficient hardware support, making it not feasible. Ternary weight quantization addresses this by constraining weights to {-1, 0, 1}, replacing expensive multiplications with hardware-efficient additions. However, such aggressive compression leads to significant accuracy degradation, even after costly quantization-aware training with massive data. We identify the core issue as _**deadzone trapping**: a large number of weights are trapped at the deadzone boundary._ This occurs because these weights receive only noisy, less informative gradients, preventing stable escape from the deadzone and severely impeding model capacity and optimization. To address this issue, we propose **Tequila**, a trapping-free quantization optimization method that reactivates deadzone-trapped weights by repurposing them as dynamic biases. This allows the repurposed weights to provide a continuous signal in the forward pass and, critically, receive direct, meaningful gradient signals during backpropagation, thereby enhancing model capacity and optimization with nearly _zero_ inference overhead. Extensive evaluations demonstrate that Tequila outperforms state-of-the-art (SOTA) ternary quantization methods across five benchmarks. Specifically, on the ARC benchmark, it achieves
2025-12-31
International Conference on Learning Representations (Accept (Poster))
openreview.net
openreview.net
The 2nd Workshop on Foundation Models for Science: Real-World Impact and Science-First Design
Wuyang Chen
Yongji Wang
N. Benjamin Erichson
Laurence Perreault-Levasseur
Bo Li
Damian Borth
Swarat Chaudhuri
Scientific foundation models should be built for science, not for generic AI tastes or leaderboard prestige. This workshop centers problem-d… (voir plus)riven design: models that measurably advance real scientific inquiries, e.g., forecasting extreme climate events, accelerating materials discovery, understanding biological mechanisms, co-developed with domain experts and validated against field data, experiments, and downstream impact. We argue that foundation models for science must be built differently from language and vision. Scientific data are physical, causal, spatiotemporal, and often scarce or biased; objectives must reflect mechanistic fidelity, not just predictive accuracy. This calls for scientific priors and constraints, robust uncertainty quantification (UQ), and architectures that natively handle multi-modality (e.g., grids, meshes, spectra, time series, point clouds, text, images, code). It also demands tight integration with classical scientific tools (simulators, PDE solvers, optimization and inference engines, and HPC workflows) to yield hybrid systems that are faster, more accurate, and more trustworthy. We will highlight opportunities and hard problems unique to science: enforcing conservation laws and symmetries; learning across vast spatial and temporal scales; representing extreme events and tipping points; calibrating and validating UQ; and developing evaluation protocols that reward mechanistic insight and actionable reliability. The goal is a roadmap for building, training, and deploying scientific foundation models that accelerate discovery while respecting the structure of the natural world.
2025-12-31
Workshop Proposals @ International Conference on Learning Representations (publié)
openreview.net
openreview.net
The Design Space of Tri-Modal Masked Diffusion Models
Louis Bethune
Victor Turrisi
Bruno Kacper Mlodozeniec
Pau Rodriguez Lopez
Lokesh Boominathan
Nikhil Bhendawade
Amitis Shidani
Joris Pelemans
Theo X. Olausson
Devon Hjelm
Paul Dixon
Joao Monteiro
Pierre Ablin
Vishnu Banna
Arno Blaas
Nick Henderson
Kari Noriy
Dan Busbridge
Josh Susskind
Marco Cuturi … (voir 4 de plus)
Irina Belousova
Luca Zappella
Russ Webb
Jason Ramapuram
Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and fine-tuni… (voir plus)ng a base unimodal model for bimodal generation. Diverging from previous approaches, we introduce the first tri-modal masked diffusion model pretrained from scratch on text, image-text, and audio-text data. We systematically analyze multimodal scaling laws, modality mixing ratios, noise schedules, and batch-size effects, and we provide optimized inference sampling defaults. Our batch-size analysis yields a novel stochastic differential equation (SDE)-based reparameterization that eliminates the need for tuning the optimal batch size as reported in recent work. This reparameterization decouples the physical batch size, often chosen based on compute constraints (GPU saturation, FLOP efficiency, wall-clock time), from the logical batch size, chosen to balance gradient variance during stochastic optimization. Finally, we pretrain a preliminary 3B-parameter tri-modal model on 6.4T tokens, demonstrating the capabilities of a unified design and achieving strong results in text generation, text-to-image tasks, and text-to-speech tasks. Our work represents the largest-scale systematic open study of multimodal discrete diffusion models conducted to date, providing insights into scaling behaviors across multiple modalities.
2025-12-31
arXiv (prépublication)
doi.org
arxiv.org