Publications

Natural language processing for African languages
David Ifeoluwa Adelani
Recent advances in word embeddings and language models use large-scale, unlabelled data and self-supervised learning to boost NLP performanc… (voir plus)e. Multilingual models, often trained on web-sourced data like Wikipedia, face challenges: few low-resource languages are included, their data is often noisy, and lack of labeled datasets makes it hard to evaluate performance outside high-resource languages like English. In this dissertation, we focus on languages spoken in Sub-Saharan Africa where all the indigenous languages in this region can be regarded as low-resourced in terms of the availability of labelled data for NLP tasks and unlabelled data found on the web. We analyse the noise in the publicly available corpora, and curate a high-quality corpus, demonstrating that the quality of semantic representations learned in word embeddings does not only depend on the amount of data but on the quality of pre-training data. We demonstrate empirically the limitations of word embeddings, and the opportunities the multilingual pre-trained language model (PLM) offers especially for languages unseen during pre-training and low-resource scenarios. We further study how to adapt and specialize multilingual PLMs to unseen African languages using a small amount of monolingual texts. To address the under-representation of the African languages in NLP research, we developed large scale human-annotated labelled datasets for 21 African languages in two impactful NLP tasks: named entity recognition and machine translation. We conduct an extensive empirical evaluation using state-of-the-art methods across supervised, weakly-supervised, and transfer learning settings.
2025-06-29
ArXiv (prépublication)
doi.org
arxiv.org
Robust and Interpretable Relational Reasoning with Large Language Models and Symbolic Solvers
Ge Zhang
Mohammad Alomrani
Hongjian Gu
Jiaming Zhou
Yaochen Hu
B. Wang
Qun Liu
Mark J. Coates
Yingxue Zhang
Jianye HAO
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational rea… (voir plus)soning problems such as kinship or spatial reasoning. In this paper, we present Path-of-Thoughts (PoT), a novel framework designed to tackle relation reasoning by decomposing the task into three key stages: graph extraction, path identification, and reasoning. Unlike previous approaches, PoT efficiently extracts a task-agnostic graph that identifies crucial entities, relations, and attributes within the problem context. Subsequently, PoT identifies relevant reasoning chains within the graph corresponding to the posed question, facilitating inference of potential answers. Experimental evaluations on four benchmark datasets, demanding long reasoning chains, demonstrate that PoT surpasses state-of-the-art baselines by a significant margin (maximum 21.3\%) without necessitating fine-tuning or extensive LLM calls. Furthermore, as opposed to prior neuro-symbolic methods, PoT exhibits improved resilience against LLM errors by leveraging the compositional nature of graphs.
2025-06-29
ICML.cc/2025/Workshop/R2-FM (poster)
openreview.net
openreview.net
Cervical Spinal Cord Magnetization Transfer Ratio and Its Relationship With Clinical Outcomes in Multiple Sclerosis
Lisa Eunyoung Lee
Julien Cohen‐Adad
Irene M. Vavasour
Melanie Guenette
Katherine Sawicka
Neda Rashidi‐Ranjbar
Nathan Churchill
Akash Chopra
Adelia Adelia
Pierre‐Louis Benveniste
Anthony Traboulsee
Nathalie Arbour
Fabrizio Giuliani
Larry D. Lynd
Scott B. Patten
Alexandre Prat
Alice Schabas
Penelope Smyth
Roger Tam
Yunyan Zhang … (voir 6 de plus)
Simon J. Graham
Mojgan Hodaie
Anthony Feinstein
Shannon Kolind
Tom A. Schweizer
Jiwon Oh
The cervical spinal cord (cSC) is highly relevant to clinical dysfunction in multiple sclerosis (MS) but remains understudied using quantita… (voir plus)tive magnetic resonance imaging (MRI). We assessed magnetization transfer ratio (MTR), a semi‐quantitative MRI measure sensitive to MS‐related tissue microstructural changes, in the cSC and its relationship with clinical outcomes in radiologically isolated syndrome (RIS) and MS. MTR data were acquired from 52 RIS, 201 relapsing–remitting MS (RRMS), 47 primary progressive MS (PPMS), and 43 control (CON) participants across four sites in the Canadian Prospective Cohort Study to Understand Progression in MS (CanProCo) using 3.0 T MRI systems. Mean MTR was compared between groups in whole cSC and sub‐regions between C2‐C4. Multiple linear regression was used to evaluate relationships between MTR and clinical outcomes, including the expanded disability status scale (EDSS), walking speed test (WST), and manual dexterity test (MDT). There were consistent group differences in MTR, which were most pronounced between PPMS and CON (−5.8% to −3.7%, p ≤ 0.01). In PPMS, lower MTR was associated with greater disability as measured by EDSS (β = −0.3 to −0.1, p ≤ 0.03), WST (β = −0.9 to −0.5, p ≤ 0.04), and MDT (β = −0.6 and − 0.5, p = 0.04). In RRMS, MTR was associated with only EDSS (β = −0.1, p ≤ 0.03). In this large sample of RIS and MS, cSC MTR was lowest in PPMS, with associations between MTR and clinical outcomes in MS but not RIS. These findings suggest that MTR provides important information about the underlying tissue microstructural integrity of the cSC relevant to clinical disability in established MS.
2025-06-28
Annals of Clinical and Translational Neurology (publié)
doi.org
A Novel Sequential Framework for Transmission Network Expansion Planning: Benders Decomposition Preceding Semidefinite Programming
Elmira Fathipasandideh
Hussein Suprême
Hanane Dagdougui
Dalal Asber
The transmission network expansion planning (TNEP) problem is inherently complex because of its nonlinear and nonconvex nature, arising from… (voir plus) the inclusion of AC power flow constraints, discrete investment decisions, and multiple operating scenarios. These characteristics make the problem computationally challenging, particulary when scaling to larger systems with multistage planning horizons. Addressing this complexity requires advanced methodologies that balance the solution accuracy and computational efficiency. This paper presents a novel two-step framework for TNEP that first applies Benders decomposition to separate investment and operational decisions, followed by semidefinite linearization to reformulate the operational subproblems. The proposed approach enhances the solution quality by ensuring convexity in the subproblems and improves computational efficiency through decomposition. Numerical results for 6- , 10-, and 24-bus test systems demonstrate that the proposed method achieves superior performance compared to existing approaches in terms of solution accuracy and computational efficiency.
2025-06-28
2025 IEEE Kiel PowerTech (publié)
doi.org
Latency-Aware Pruning and Quantization of Self-Supervised Speech Transformers for Edge Devices
Seyed Milad Ebrahimipour
Seyyed Hasan Mozafari
James J. Clark
Warren J. Gross
Brett H. Meyer
The growing adoption of self-supervised learning transformers for speech (speech SSL) is constrained by their significant computational and … (voir plus)memory demands, making deployment on resource-constrained edge devices challenging. We propose a latency-aware compression framework that integrates structured pruning and quantization to address these challenges. Guided by a latency model that considers the combined effects of pruning and quantization, our method dynamically identifies and removes less critical blocks while maintaining task performance, avoiding the inefficiencies of over-pruning and under-pruning seen in prior approaches. Unlike prior methods specialized in either post-training compression without fine-tuning data or in cases where fine-tuning data is available, our method is effective in both settings. Experimental results show that, in task-agnostic compression, our method achieves a 4.2 × speedup on the Hikey970 edge development platform, outperforming previous task-agnostic pruning methods in most tasks, while requiring only 21–24 GPU hours—a 3 × reduction compared to prior methods. Additionally, our method achieves a lower word error rate of 7.8% using task-specific pruning, while reducing computational overhead by approximately 19.4% in terms of GFLOPs compared to previous task-specific methods. Finally, our method consistently achieves higher accuracy than the state-of-the-art post-training compression approach across various latency speedup constraints, even without fine-tuning data.
2025-06-27
ACM Transactions on Embedded Computing Systems (publié)
doi.org
Small Encoders Can Rival Large Decoders in Detecting Groundedness
Istabrak Abbes
Gabriele Prato
Quentin Fournier
Fernando Rodriguez
Alaa Boukhary
Adam Elwood
A. Chandar
Augmenting large language models (LLMs) with external context significantly improves their performance in natural language processing (NLP) … (voir plus)tasks. However, LLMs struggle to answer queries reliably when the provided context lacks information, often resorting to ungrounded speculation or internal knowledge. Groundedness - generating responses strictly supported by the context - is essential for ensuring factual consistency and trustworthiness. This study focuses on detecting whether a given query is grounded in a document provided in context before the costly answer generation by LLMs. Such a detection mechanism can significantly reduce both inference time and resource consumption. We show that lightweight, task specific encoder models such as RoBERTa and NomicBERT, fine-tuned on curated datasets, can achieve accuracy comparable to state-of-the-art LLMs, such as Llama3 8B and GPT4o, in groundedness detection while reducing inference latency by orders of magnitude. The code is available at : https://github.com/chandarlab/Hallucinate-less
2025-06-25
ArXiv (prépublication)
doi.org
arxiv.org
T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs
Alireza Dizaji
Benedict Aaron Tjandra
Mehrab Hamidi
Shenyang Huang
Guillaume Rabusseau
Dynamic graph learning methods have recently emerged as powerful tools for modelling relational data evolving through time. However, despite… (voir plus) extensive benchmarking efforts, it remains unclear whether current Temporal Graph Neural Networks (TGNNs) effectively capture core temporal patterns such as periodicity, cause-and-effect, and long-range dependencies. In this work, we introduce the Temporal Graph Reasoning Benchmark (T-GRAB), a comprehensive set of synthetic tasks designed to systematically probe the capabilities of TGNNs to reason across time. T-GRAB provides controlled, interpretable tasks that isolate key temporal skills: counting/memorizing periodic repetitions, inferring delayed causal effects, and capturing long-range dependencies over both spatial and temporal dimensions. We evaluate 11 temporal graph learning methods on these tasks, revealing fundamental shortcomings in their ability to generalize temporal patterns. Our findings offer actionable insights into the limitations of current models, highlight challenges hidden by traditional real-world benchmarks, and motivate the development of architectures with stronger temporal reasoning abilities. The code for T-GRAB can be found at: https://github.com/alirezadizaji/T-GRAB.
2025-06-25
MLoG-GenAI @ ACM SIGKDD Conference on Knowledge Discovery and Data Mining (présentation orale)
doi.org
openreview.net
Inter-brain Synchronization in the Alpha Band during Minimal Tactile Interaction
Chen Lam Loh
Leonardo Zapata-Fonseca
Mark M. James
Guillaume Dumas
Tom Froese
Recently the minimal requirements for inter-brain coupling have attracted attention. Moreover, researchers have found that brains can couple… (voir plus) not only when individuals are in the same space, but also during technologically mediated interactions. Here we investigate whether inter-brain synchronization occurs when both conditions of spatial isolation and minimal interaction are satisfied. In particular, we use a real-time interaction paradigm, the Perceptual Crossing Experiment where individuals must locate their partners in a minimal virtual space using tactile stimuli alone. We report novel findings that contribute to our understanding of inter-brain synchronization and the minimal conditions of social interaction in virtual spaces : 1)inter-brain synchronization is present in the Alpha band during online minimal interaction, 2)five behavioral patterns and three inter-brain patterns can be found in the PCE and 3)different behavioral patterns in the interaction environment recruited different inter-brain networks such that frontal-fronto-central synchrony occurs when people are further apart in space, interacting with a multitude of objects. These findings have important implications for the understanding of social interaction processes, such that inter-brain coupling can occur even without extensive communication channels.
2025-06-23
bioRxiv (prépublication)
doi.org
Prompt learning with bounding box constraints for medical image segmentation.
Melanie Gaillochet
Mehrdad Noori
Sahar Dastani
Christian Desrosiers
Hervé Lombaert
Pixel-wise annotations are notoriously labourious and costly to obtain in the medical domain. To mitigate this burden, weakly supervised app… (voir plus)roaches based on bounding box annotations-much easier to acquire-offer a practical alternative. Vision foundation models have recently shown noteworthy segmentation performance when provided with prompts such as points or bounding boxes. Prompt learning exploits these models by adapting them to downstream tasks and automating segmentation, thereby reducing user intervention. However, existing prompt learning approaches depend on fully annotated segmentation masks. This paper proposes a novel framework that combines the representational power of foundation models with the annotation efficiency of weakly supervised segmentation. More specifically, our approach automates prompt generation for foundation models using only bounding box annotations. Our proposed optimization scheme integrates multiple constraints derived from box annotations with pseudo-labels generated by the prompted foundation model. Extensive experiments across multi-modal datasets reveal that our weakly supervised method achieves an average Dice score of 84.90% in a limited data setting, outperforming existing fully-supervised and weakly-supervised approaches. The code will be available upon acceptance
2025-06-23
IEEE transactions on bio-medical engineering (publié)
doi.org
arxiv.org
Spatially and non-spatially tuned hippocampal neurons are linear perceptual and nonlinear memory encoders
Maxime Daigle
Kaicheng Yan
Benjamin Corrigan
Roberto Gulli
Julio Martinez-Trujillo
Pouya Bashivan
The hippocampus has long been regarded as a neural map of physical space, with its neurons categorized as spatially or non-spatially tuned a… (voir plus)ccording to their response selectivity. However, growing evidence suggests that this dichotomy oversimplifies the complex roles hippocampal neurons play in integrating spatial and non-spatial information. Through computational modeling and in-vivo electrophysiology in macaques, we show that neurons classified as spatially tuned primarily encode linear combinations of immediate behaviorally relevant factors, while those labeled as non-spatially tuned rely on nonlinear mechanisms to integrate temporally distant experiences. Furthermore, our findings reveal a temporal gradient in the primate CA3 region, where spatial selectivity diminishes as neurons encode increasingly distant past events. Finally, using artificial neural networks, we demonstrate that nonlinear recurrent connections are crucial for capturing the response dynamics of non-spatially tuned neurons, particularly those encoding memory-related information. These findings challenge the traditional dichotomy of spatial versus non-spatial representations and instead suggest a continuum of linear and nonlinear computations that underpin hippocampal function. This framework provides new insights into how the hippocampus bridges perception and memory, informing on its role in episodic memory, spatial navigation, and associative learning.
2025-06-23
bioRxiv (prépublication)
doi.org
CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer
Qifei Wang
He Zhu
Yiwen Hu
Yanjie Chen
Yuwei Wang
Guochao Li
Yun Li
Jinfeng Chen
Xuegong Zhang
James Zou
Manolis Kellis
Yue Li
Dianbo Liu
Lan Jiang
Identifying the genetic and molecular drivers of phenotypic heterogeneity among individuals is vital for understanding human health and for … (voir plus)diagnosing, monitoring, and treating diseases. To this end, international consortia such as the Human Cell Atlas and the Tabula Sapiens are creating comprehensive cellular references. Due to the massive volume of data generated, machine learning methods, especially transformer architectures, have been widely employed in related studies. However, applying machine learning to cellular data presents several challenges. One such challenge is making the methods interpretable with respect to both the input cellular information and its context. Another less explored challenge is the accurate representation of cells outside existing references, referred to as out-of-distribution (OOD) cells. The out-of-distribution could be attributed to various physiological conditions, such as comparing diseased cells, particularly tumor cells, with healthy reference data, or significant technical variations, such as using transfer learning from single-cell reference to spatial query data. Inspired by the global workspace theory in cognitive neuroscience, we introduce CellMemory, a bottlenecked Transformer with improved generalization capabilities designed for the hierarchical interpretation of OOD cells unseen during reference building. Even without pre-training, it exceeds the performance of large language models pre-trained with tens of millions of cells. In particular, when deciphering spatially resolved single-cell transcriptomics data, CellMemory demonstrates the ability to interpret data at the granule level accurately. Finally, we harness CellMemory's robust representational capabilities to elucidate malignant cells and their founder cells in different patients, providing reliable characterizations of the cellular changes caused by the disease.
2025-06-22
Genome Biology (publié)
doi.org
Learning to combine top-down context and feed-forward representations under ambiguity with apical and basal dendrites
Nizar Islah
Guillaume Etter
Mashbayar Tugsbayar
Busra Tugce Gurbuz
Blake Richards
Eilif B. Muller
One of the hallmark features of neocortical anatomy is the presence of extensive top-down projections into primary sensory areas, with many … (voir plus)impinging on the distal apical dendrites of pyramidal neurons. While it is known that they exert a modulatory effect, altering the gain of responses, their functional role remains an active area of research. It is hypothesized that these top-down projections carry contextual information that can help animals to resolve ambiguities in sensory data. One proposed mechanism of contextual integration is a non-linear integration of distinct input streams at apical and basal dendrites of pyramidal neurons. Computationally, however, it is yet to be demonstrated how such an architecture could leverage distinct compartments for flexible contextual integration and sensory processing when both sensory and context signals can be unreliable. Here, we implement an augmented deep neural network with distinct apical and basal compartments that integrates a) contextual information from top-down projections to apical compartments, and b) sensory representations driven by bottom-up projections to basal compartments, via a biophysically inspired rule. In addition, we develop a new multi-scenario contextual integration task using a generative image modeling approach. In addition to generalizing previous contextual integration tasks, it better captures the diversity of scenarios where neither contextual nor sensory information are fully reliable. To solve this task, this model successfully learns to select among integration strategies. We find that our model outperforms those without the "apical prior" when contextual information contradicts sensory input. Altogether, this suggests that the apical prior and biophysically inspired integration rule could be key components necessary for handling the ambiguities that animals encounter in the diverse contexts of the real world.
2025-06-22
Cerebral Cortex (New York, NY) (publié)
doi.org
arxiv.org