Publications

THUNDER: Tile-level Histopathology image UNDERstanding benchmark
Pierre Marza
Leo Fillioux
Sofiène Boutaj
KUNAL MAHATHA
Christian Desrosiers
Pablo Piantanida
Jose Dolz
Stergios Christodoulidis
Maria Vakalopoulou
Progress in a research field can be hard to assess, in particular when many concurrent methods are proposed in a short period of time. This … (voir plus)is the case in digital pathology, where many foundation models have been released recently to serve as feature extractors for tile-level images, being used in a variety of downstream tasks, both for tile- and slide-level problems. Benchmarking available methods then becomes paramount to get a clearer view of the research landscape. In particular, in critical domains such as healthcare, a benchmark should not only focus on evaluating downstream performance, but also provide insights about the main differences between methods, and importantly, further consider uncertainty and robustness to ensure a reliable usage of proposed models. For these reasons, we introduce THUNDER, a tile-level benchmark for digital pathology foundation models, allowing for efficient comparison of many models on diverse datasets with a series of downstream tasks, studying their feature spaces and assessing the robustness and uncertainty of predictions informed by their embeddings. THUNDER is a fast, easy-to-use, dynamic benchmark that can already support a large variety of state-of-the-art foundation, as well as local user-defined models for direct tile-based comparison. In this paper, we provide a comprehensive comparison of 23 foundation models on 16 different datasets covering diverse tasks, feature analysis, and robustness. The code for THUNDER is publicly available at https://github.com/MICS-Lab/thunder.
2025-09-17
NeurIPS.cc/2025/Datasets_and_Benchmarks_Track (spotlight)
openreview.net
openreview.net
On Traceability in $\ell_p$ Stochastic Convex Optimization
Sasha Voitovych
MAHDI HAGHIFAM
Idan Attias
Gintare Karolina Dziugaite
Roi Livni
Daniel M. Roy
In this paper, we investigate the necessity of traceability for accurate learning in stochastic convex optimization (SCO) under …
2025-09-17
NeurIPS.cc/2025/Conference (spotlight)
doi.org
openreview.net
Transforming Generic Coder LLMs to Effective Binary Code Embedding Models for Similarity Detection
Litao Li
Leo Song
Steven Ding
Benjamin C. M. Fung
Philippe Charland
Cybersecurity and software research have crossed paths with modern deep learning research for a few years. The power of large language model… (voir plus)s (LLMs) in particular has intrigued us to apply them to understanding binary code. In this paper, we investigate some of the many ways LLMs can be applied to binary code similarity detection, as it is a significantly more difficult task compared to source code similarity detection due to the sparsity of information and less meaningful syntax. It also has great practical implications, such as vulnerability and malware detection. We find that pretrained LLMs are mostly capable of detecting similar binary code, even with a zero-shot setting. Our main contributions and findings are to provide several supervised fine-tuning methods that, when combined, significantly surpass zero-shot LLMs and state-of-the-art binary code similarity detection methods. Specifically, we up-train the model through data augmentation, translation-style causal learning, LLM2Vec, and cumulative GTE loss. With a complete ablation study, we show that our training method can transform a generic language model into a powerful binary similarity expert, and is also robust and general enough for cross-optimization, cross-architecture, and cross-obfuscation detection.
2025-09-17
NeurIPS.cc/2025/Conference (poster)
openreview.net
openreview.net
CellSexID: Sex-Based Computational Tracking of Cellular Origins in Chimeric Models
Huilin Tai
Qian Li
Jingtao Wang
Jiahui Tan
Bowen Zhao
Ryann Lang
Basil J. Petrof
Jun Ding
Cell tracking in chimeric models is essential yet challenging, particularly in developmental biology, regenerative medicine, and transplanta… (voir plus)tion studies. Existing methods, such as fluorescent labeling and genetic barcoding, are technically demanding, costly, and often impractical for dynamic, heterogeneous tissues. To address these limitations, we propose a computational framework that leverages sex as a surrogate marker for cell tracking. Our approach uses a machine learning model trained on single-cell transcriptomic data to predict cell sex with high accuracy, enabling clear distinction between donor (male) and recipient (female) cells in sex-mismatched chimeric models. The model identifies specific genes critical for sex prediction and has been validated using public datasets and experimental flow sorting, confirming the biological relevance of the identified cell populations. Applied to skeletal muscle macrophages, our method revealed distinct transcriptional profiles associated with cellular origins. This pipeline offers a robust, cost-effective solution for cell tracking in chimeric models, advancing research in regenerative medicine and immunology by providing precise insights into cellular origins and therapeutic outcomes.
2025-09-16
Cell Reports Methods (publié)
doi.org
Combining cortical and spinal stimulation maximizes improvement of gait after spinal cord injury
Roxanne Drainville
Marco Bonizzato
Davide Burchielli
Rose Guay-Hottin
Alexandre Sheasby
Marina Martinez
Most spinal cord injuries (SCI) spare descending motor pathways and sublesional networks, which can be activated through motor cortex and sp… (voir plus)inal cord stimulation to mitigate locomotor deficits. However, the potential synergy between cortical and spinal stimulation as a neuroprosthetic intervention remains unknown. Here, we first investigated phase-locked electrical stimulation of the motor cortex and lumbar spinal cord at 40 Hz in a rat model of unilateral SCI. Combining cortical and lumbar stimulation around the anticipated lift synergistically enhanced leg movements. When integrated into rehabilitation training, cortical stimulation proved essential for recovery of skilled locomotion. As a further refinement, we next investigated the effects of high-frequency (330 Hz) lumbar and sacral stimulation combined with cortical stimulation. Timely integration during the swing phase showed that cortical and rostral lumbar stimulations enhance the initial and mid-swing phases, while sacral stimulation improves extension velocity in the late swing. These findings indicate that supraspinal and sublesional neuromodulation offer complementary neuroprosthetic effects in targeted SCI gait rehabilitation. Cortical and spinal stimulations summate motor outputs via distinct pathways. Each improves gait post-SCI, but combined stimulation maximizes gait improvement. Integrating cortico-spinal stimulation into rehabilitation promotes lasting recovery. EES capabilities extended using high-frequency lumbosacral protocols.
2025-09-16
iScience (publié)
doi.org
RootletSeg: Deep learning method for spinal rootlets segmentation across MRI contrasts
Katerina Krejci
Jiri Chmelik
Sandrine B'edard
Falk Eippert
Ulrike Horn
Virginie Callot
Julien Cohen-Adad
Jan Valosek
Purpose: To develop a deep learning method for the automatic segmentation of spinal nerve rootlets on various MRI scans. Material and Method… (voir plus)s: This retrospective study included MRI scans from two open-access and one private dataset, consisting of 3D isotropic 3T TSE T2-weighted (T2w) and 7T MP2RAGE (T1-weighted [T1w] INV1 and INV2, and UNIT1) MRI scans. A deep learning model, RootletSeg, was developed to segment C2-T1 dorsal and ventral spinal rootlets. Training was performed on 76 scans and testing on 17 scans. The Dice score was used to compare the model performance with an existing open-source method. Spinal levels derived from RootletSeg segmentations were compared with vertebral levels defined by intervertebral discs using Bland-Altman analysis. Results: The RootletSeg model developed on 93 MRI scans from 50 healthy adults (mean age, 28.70 years
2025-09-16
ArXiv (prépublication)
arxiv.org
arxiv.org
GROOD: Gradient-Aware Out-of-Distribution Detection
Mostafa Elaraby
Sabyasachi Sahoo
Yann Batiste Pequignot
Paul Novello
Liam Paull
2025-09-15
TMLR (accepté)
doi.org
openreview.net
"On the goals of linguistic theory": Revisiting Chomskyan theories in the era of AI
Eva Portelance
Masoud Jasbi
Theoretical linguistics seeks to explain what human language is, and why. Linguists and cognitive scientists have proposed different theoret… (voir plus)ical models of what language is, as well as cognitive factors that shape it, and allow humans to 'produce', 'understand', and 'acquire' natural languages. However, humans may no longer be the only ones learning to 'generate', 'parse', and 'learn' natural language: artificial intelligence (AI) models such as large language models are proving to have impressive linguistic capabilities. Many are thus questioning what role, if any, such models should play in helping theoretical linguistics reach its ultimate research goals? In this paper, we propose to answer this question, by reiterating the tenets of generative linguistics, a leading school of thought in the field, and by considering how AI models as theories of language relate to each of these important concepts. Specifically, we consider three foundational principles, finding roots in the early works of Noam Chomsky: (1) levels of theoretical adequacy; (2) procedures for linguistic theory development; (3) language learnability and Universal Grammar. In our discussions of each principle, we give special attention to two types of AI models: neural language models and neural grammar induction models. We will argue that such models, in particular neural grammar induction models, do have a role to play, but that this role is largely modulated by the stance one takes regarding each of these three guiding principles.
2025-09-15
Nature Computational Science (publié)
doi.org
arxiv.org
caskade: building Pythonic scientific simulators
Connor Stone
Alexandre Adam
Adam Coogan
Laurence Perreault-Levasseur
Yashar Hezaveh
2025-09-14
Journal of Open Source Software (publié)
doi.org
Low Compute Unlearning via Sparse Representations
Vedant Shah
Frederik Träuble
Ashish Malik
Hugo Larochelle
Michael Curtis Mozer
Sanjeev Arora
Yoshua Bengio
Anirudh Goyal
Machine unlearning, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible … (voir plus)using existing techniques. We propose a low-compute unlearning technique based on a discrete representational bottleneck. We show that the proposed technique efficiently unlearns the forget set and incurs negligible damage to the model's performance on the rest of the dataset. We evaluate the proposed technique on the problem of class unlearning using four datasets: CIFAR-10, CIFAR-100, LACUNA-100 and ImageNet-1k. We compare the proposed technique to SCRUB, a state-of-the-art approach which uses knowledge distillation for unlearning. Across all four datasets, the proposed technique performs as well as, if not better than SCRUB while incurring almost no computational cost.
2025-09-14
Transactions on Machine Learning Research (accepté)
doi.org
openreview.net
Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training
Hiroki Naganuma
Xinzhi Zhang
Man-Chung Yue
Ioannis Mitliagkas
Russell J. Hewett
Philipp Andre Witte
Yin Tat Lee
2025-09-14
TMLR (accepté)
openreview.net
openreview.net
Hitting the right pitch: Cortical tracking of speech fundamental frequency in auditory and somatomotor regions
Yorguin-Jose Mantilla-Ramos
Ana-Sofía Hincapié-Casas
Annalisa Pascarella
Tarek Lajnef
Richard M. Leahy
Emily B.J. Coffey
Karim Jerbi CoCo Lab
Véronique Boulenger
Low-frequency neural oscillations contribute to the parsing of continuous speech into linguistic units. Little is known however on the coupl… (voir plus)ing of brain rhythms to higher-frequencies in speech such as fundamental frequency (F0) or pitch. Using magnetoencephalography, we investigated whole-brain cortical tracking of F0 while participants listened to sentences produced at normal rate or fast rate, where pitch naturally increases, and to artificially accelerated sentences, where F0 remains unchanged. Our results revealed significant brain-to-F0 coupling across all speech rates not only in right auditory but also in right parietal, insular, and pre- and postcentral regions, likely including the ventral larynx area. Importantly, the cortico-acoustic coupling peak frequency was higher for natural fast speech to reflect the corresponding F0 increase compared to normal rate and time-compressed speech. These findings demonstrate the engagement of an auditory-somato-motor network in F0 tracking, supporting its role in facilitating phonemic processing during the perception of naturally-produced speech.
2025-09-12
bioRxiv (prépublication)
doi.org