Publications

An interpretable and reliable framework for alloy discovery in thermomechanical processing
Sushant Sinha
Xiaoping Ma
Kashif Rehman
Narges Armanfard
Stephen Yue
2025-03-31
Materials Today Communications (published)
doi.org
Learning from Stochastic Teacher Representations Using Student-Guided Knowledge Distillation
Muhammad Haseeb Aslam
Clara Martinez
Marco Pedersoli
Alessandro Lameiras Koerich
Ali Etemad
Eric Granger
Advances in self-distillation have shown that when knowledge is distilled from a teacher to a student using the same deep learning (DL) arch… (see more)itecture, the student performance can surpass the teacher particularly when the network is overparameterized and the teacher is trained with early stopping. Alternatively, ensemble learning also improves performance, although training, storing, and deploying multiple models becomes impractical as the number of models grows. Even distilling an ensemble to a single student model or weight averaging methods first requires training of multiple teacher models and does not fully leverage the inherent stochasticity for generating and distilling diversity in DL models. These constraints are particularly prohibitive in resource-constrained or latency-sensitive applications such as wearable devices. This paper proposes to train only one model and generate multiple diverse teacher representations using distillation-time dropout. However, generating these representations stochastically leads to noisy representations that are misaligned with the learned task. To overcome this problem, a novel stochastic self-distillation (SSD) training strategy is introduced for filtering and weighting teacher representation to distill from task-relevant representations only, using student-guided knowledge distillation (SGKD). The student representation at each distillation step is used as authority to guide the distillation process. Experimental results on real-world affective computing, wearable/biosignal datasets from the UCR Archive, the HAR dataset, and image classification datasets show that the proposed SSD method can outperform state-of-the-art methods without increasing the model size at both training and testing time, and incurs negligible computational complexity compared to state-of-the-art ensemble learning and weight averaging methods.
2025-03-31
arXiv (published)
doi.org
arxiv.org
Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things
Saeid Jamshidi
Amin Nikanjam
Kawser Wazed Nafi
Foutse Khomh
As the Internet of Things (IoT) continues to expand, ensuring the security of connected devices has become increasingly critical. Traditiona… (see more)l Intrusion Detection Systems (IDS) often fall short in managing the dynamic and large-scale nature of IoT networks. This paper explores how Machine Learning (ML) and Deep Learning (DL) techniques can significantly enhance IDS performance in IoT environments. We provide a thorough overview of various IDS deployment strategies and categorize the types of intrusions common in IoT systems. A range of ML methods -- including Support Vector Machines, Naive Bayes, K-Nearest Neighbors, Decision Trees, and Random Forests -- are examined alongside advanced DL models such as LSTM, CNN, Autoencoders, RNNs, and Deep Belief Networks. Each technique is evaluated based on its accuracy, efficiency, and suitability for real-world IoT applications. We also address major challenges such as high false positive rates, data imbalance, encrypted traffic analysis, and the resource constraints of IoT devices. In addition, we highlight the emerging role of Generative AI and Large Language Models (LLMs) in improving threat detection, automating responses, and generating intelligent security policies. Finally, we discuss ethical and privacy concerns, underscoring the need for responsible and transparent implementation. This paper aims to provide a comprehensive framework for developing adaptive, intelligent, and secure IDS solutions tailored for the evolving landscape of IoT.
2025-03-31
arXiv (published)
doi.org
arxiv.org
LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities
Thomas Schmied
Jörg Bornschein
Jordi Grau-Moya
Markus Wulfmeier
Razvan Pascanu
2025-03-31
arXiv (published)
doi.org
arxiv.org
LLMs for Literature Review: Are we there yet?
Shubham Agarwal
Gaurav Sahu
Abhay Puri
Issam Hadj Laradji
Krishnamurthy Dj Dvijotham
Jason Stanley
Laurent Charlin
Christopher Pal
Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially du… (see more)e to the recent influx of research papers. This paper explores the zero-shot abilities of recent Large Language Models (LLMs) in assisting with the writing of literature reviews based on an abstract. We decompose the task into two components: 1. Retrieving related works given a query abstract, and 2. Writing a literature review based on the retrieved results. We analyze how effective LLMs are for both components. For retrieval, we introduce a novel two-step search strategy that first uses an LLM to extract meaningful keywords from the abstract of a paper and then retrieves potentially relevant papers by querying an external knowledge base. Additionally, we study a prompting-based re-ranking mechanism with attribution and show that re-ranking doubles the normalized recall compared to naive search methods, while providing insights into the LLM's decision-making process. In the generation phase, we propose a two-step approach that first outlines a plan for the review and then executes steps in the plan to generate the actual review. To evaluate different LLM-based literature review methods, we create test sets from arXiv papers using a protocol designed for rolling use with newly released LLMs to avoid test set contamination in zero-shot evaluations. We release this evaluation protocol to promote additional research and development in this regard. Our empirical results suggest that LLMs show promising potential for writing literature reviews when the task is decomposed into smaller components of retrieval and planning. Further, we demonstrate that our planning-based approach achieves higher-quality reviews by minimizing hallucinated references in the generated review by 18-26% compared to existing simpler LLM-based generation methods.
2025-03-31
TMLR (accepted)
doi.org
openreview.net
Lugha-Llama: Adapting Large Language Models for African Languages
Happy Buzaaba
Alexander Wettig
David Ifeoluwa Adelani
Christiane Fellbaum
2025-03-31
arXiv (published)
doi.org
arxiv.org
Multiple-model coding scheme for electrical signal compression
Corentin Presvôts
Michel Kieffer
Thibault Prevost
Patrick Panciatici
Zuxing Li
Pablo Piantanida
2025-03-31
Signal Processing (published)
doi.org
Negative Language Transfer Identification in the English Writing of Chinese and Farsi Native Speakers
Mohammad Karimiabdolmaleki
Leticia Farias Wanderley
Maria Cutumisu
Mohsen Rezazadeh
Carrie Demmans Epp
2025-03-31
International Journal of Artificial Intelligence in Education (published)
doi.org
Neural Kinematic Bases for Fluids
Yibo Liu
Paul Kry
Kenny Erleben
Noam Aigerman
Sune Darkner
Teseo Schneider
2025-03-31
arXiv (published)
doi.org
arxiv.org
Neurophysiological effects of targeting sleep spindles with closed-loop auditory stimulation
Hugo R Jourde
Milo Sobral
Giovanni Beltrame
Emily B J Coffey
Sleep spindles are neural events unique to nonrapid eye movement sleep that play key roles in memory reactivation and consolidation. However… (see more), much of the evidence for their function remains correlational rather than causal. Closed-loop brain stimulation uses real-time monitoring of neural events (often via electroencephalography; EEG) to deliver precise auditory, magnetic, or electrical stimulation for research or therapeutic purposes. Automated online algorithms to detect and stimulate sleep spindles have recently been validated, but the time- and frequency-resolved physiological responses generated by them have not yet been documented. Building on the recent findings that sleep spindles do not block the transmission of sound to cortex, the present work investigates the neurophysiological responses to closed-loop auditory stimulation of sleep spindles. EEG data were collected from 10 healthy human adults (6 nights each), whilst sleep spindles were detected and in half the nights, targeted with auditory stimulation. Spindles were successfully stimulated before their offset in 97.6% of detections and did not disturb sleep. Comparing stimulation with sham, we observed that stimulation resulted in increased sigma activity (11–16 Hz) at about 1 second poststimulation but that stimulation occurring at the beginning of the spindle also resulted in early termination of the spindle. Finally, we observed that stimulating an evoked spindle did not elicit additional sigma activity. Our results validate the use of closed-loop auditory stimulation targeting sleep spindles, and document its neural effects, as a basis for future causal investigations concerning spindles’ roles in memory consolidation.
2025-03-31
Sleep Advances (published)
doi.org
Online Interior-point Methods for Time-varying Equality-constrained Optimization
Jean-Luc Lupien
Iman Shames
Antoine Lesage-Landry
2025-03-31
IEEE Transactions on Automatic Control (published)
doi.org
arxiv.org
Performance Smells in ML and Non-ML Python Projects: A Comparative Study
Franccois Belias
Leuson Da Silva
Foutse Khomh
Cyrine Zid
2025-03-31
arXiv (published)
doi.org
arxiv.org