Narges Armanfard

Biographie

Narges Armanfard (Ph. D., ing.) est la fondatrice et la chercheuse principale du laboratoire iSMART. Elle est professeure adjointe au Département de génie électrique et informatique de l'Université McGill et membre académique associé à Mila – Institut québécois d'intelligence artificielle. Elle est également affiliée au Centre sur les machines intelligentes de McGill (CIM), à l'Initiative de McGill en médecine computationnelle (MiCM) et à l'Institut de génie aérospatial de McGill (MIAE). Sa recherche porte sur le développement d'algorithmes novateurs pour divers domaines tels que l'analyse de données de séries temporelles, la vision par ordinateur, l'apprentissage par renforcement et l'apprentissage par représentation pour des tâches telles que le regroupement de données, la classification et la détection d'anomalies. Ses contributions au domaine de l'IA ont été reconnues par de nombreux prix, décernés notamment par le Conseil de recherches en sciences naturelles et en génie du Canada, AgeWell, Vanier-Banting, les Fonds de recherche du Québec, ainsi que l'Université McMaster, l'Université McGill, l'Université de Toronto, les Instituts de recherche en santé du Canada et Scale AI.

Étudiants actuels

Bahar Bahareh Nikpour

Doctorat - McGill

Cormac Cureton

Maîtrise recherche - McGill

Github

Khanh Ho Ho

Doctorat - McGill

Hadi Hojjati

Doctorat - McGill

Ali Karami

Maîtrise recherche - McGill

Github

Thomas Lai

Doctorat - McGill

Abhijeet Praveen

Maîtrise recherche - McGill

Doctorat - McGill

Doctorat - McGill

Zihan Wang

Maîtrise recherche - McGill

Co-superviseur⋅e :

Samira Ebrahimi Kahou

Site web

Jack Wei

Maîtrise recherche - McGill

Github

Publications

Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection

Alex Koran

Hadi Hojjati

Deep learning for time-series anomaly detection (TSAD) has gained significant attention over the past decade. Despite the reported improveme… (voir plus)nts in several papers, the practical application of these models remains limited. Recent studies have cast doubt on these models, attributing their results to flawed evaluation techniques. However, the impact of initialization has largely been overlooked. This paper provides a critical analysis of the initialization effects on TSAD model performance. Our extensive experiments reveal that TSAD models are highly sensitive to hyperparameters such as window size, seed number, and normalization. This sensitivity often leads to significant variability in performance, which can be exploited to artificially inflate the reported efficacy of these models. We demonstrate that even minor changes in initialization parameters can result in performance variations that overshadow the claimed improvements from novel model architectures. Our findings highlight the need for rigorous evaluation protocols and transparent reporting of preprocessing steps to ensure the reliability and fairness of anomaly detection methods. This paper calls for a more cautious interpretation of TSAD advancements and encourages the development of more robust and transparent evaluation practices to advance the field and its practical applications.

2024-08-13

ArXiv (prépublication)

Forward-Backward Knowledge Distillation for Continual Clustering

Mohammadreza Sadeghi

Zihan Wang

Unsupervised Continual Learning (UCL) is a burgeoning field in machine learning, focusing on enabling neural networks to sequentially learn … (voir plus)tasks without explicit label information. Catastrophic Forgetting (CF), where models forget previously learned tasks upon learning new ones, poses a significant challenge in continual learning, especially in UCL, where labeled information of data is not accessible. CF mitigation strategies, such as knowledge distillation and replay buffers, often face memory inefficiency and privacy issues. Although current research in UCL has endeavored to refine data representations and address CF in streaming data contexts, there is a noticeable lack of algorithms specifically designed for unsupervised clustering. To fill this gap, in this paper, we introduce the concept of Unsupervised Continual Clustering (UCC). We propose Forward-Backward Knowledge Distillation for unsupervised Continual Clustering (FBCC) to counteract CF within the context of UCC. FBCC employs a single continual learner (the ``teacher'') with a cluster projector, along with multiple student models, to address the CF issue. The proposed method consists of two phases: Forward Knowledge Distillation, where the teacher learns new clusters while retaining knowledge from previous tasks with guidance from specialized student models, and Backward Knowledge Distillation, where a student model mimics the teacher's behavior to retain task-specific knowledge, aiding the teacher in subsequent tasks. FBCC marks a pioneering approach to UCC, demonstrating enhanced performance and memory efficiency in clustering across various tasks, outperforming the application of clustering algorithms to the latent space of state-of-the-art UCL algorithms.

2024-05-29

ArXiv (prépublication)

Deep Clustering with Self-Supervision using Pairwise Similarities

Mohammadreza Sadeghi

Deep clustering incorporates embedding into clustering to find a lower-dimensional space appropriate for clustering. In this paper, we propo… (voir plus)se a novel deep clustering framework with self-supervision using pairwise similarities (DCSS). The proposed method consists of two successive phases. In the first phase, we propose to form hypersphere-like groups of similar data points, i.e. one hypersphere per cluster, employing an autoencoder that is trained using cluster-specific losses. The hyper-spheres are formed in the autoencoder's latent space. In the second phase, we propose to employ pairwise similarities to create a

2024-05-06

ArXiv (prépublication)

AI healthcare research: Pioneering iSMART Lab

Dr Narges Armanfard, Professor, talks us through the AI healthcare research at McGill University which is spearheading a groundbreaking init… (voir plus)iative – the iSMART Lab. Access to high-quality healthcare is not just a fundamental human right; it is the bedrock of our societal wellbeing, with the crucial roles played by doctors, nurses, and hospitals. Yet, healthcare systems globally face mounting challenges, particularly from aging populations. Dr Narges Armanfard, affiliated with McGill University and Mila Quebec AI Institute in Montreal, Canada, has spearheaded a groundbreaking initiative – the iSMART Lab. This laboratory represents a revolutionary leap into the future of healthcare, with its pioneering research in AI for health applications garnering significant attention. Renowned for its innovative integration of AI across diverse domains, iSMART Lab stands at the forefront of harnessing Artificial Intelligence to elevate and streamline health services.

2024-04-09

Open Access Government (publié)

Self-supervised anomaly detection in computer vision and beyond: A survey and outlook.

Hadi Hojjati

Thi Kieu Khanh Ho

2024-04-01

Neural Networks (publié)

Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection

Ali Karami

Thi Kieu Khanh Ho

2024-03-18

ArXiv (prépublication)

Neural network prediction of the effect of thermomechanical controlled processing on mechanical properties

Sushant Sinha

Denzel Guye

Xiaoping Ma

Kashif Rehman

S. Yue

2024-03-01

Machine Learning with Applications (publié)

Deep Reinforcement Learning in Human Activity Recognition: A Survey and Outlook.

Bahareh Nikpour

Dimitrios Sinodinos

Human activity recognition (HAR) is a popular research field in computer vision that has already been widely studied. However, it is still a… (voir plus)n active research field since it plays an important role in many current and emerging real-world intelligent systems, like visual surveillance and human-computer interaction. Deep reinforcement learning (DRL) has recently been used to address the activity recognition problem with various purposes, such as finding attention in video data or obtaining the best network structure. DRL-based HAR has only been around for a short time, and it is a challenging, novel field of study. Therefore, to facilitate further research in this area, we have constructed a comprehensive survey on activity recognition methods that incorporate DRL. Throughout the article, we classify these methods according to their shared objectives and delve into how they are ingeniously framed within the DRL framework. As we navigate through the survey, we conclude by shedding light on the prominent challenges and lingering questions that await the attention of future researchers, paving the way for further advancements and breakthroughs in this exciting domain.

2024-02-19

IEEE Transactions on Neural Networks and Learning Systems (publié)

EMA-Net: Efficient Multitask Affinity Learning for Dense Scene Predictions

Dimitrios Sinodinos

2024-01-20

ArXiv (prépublication)

Open-Set Multivariate Time-Series Anomaly Detection

Thomas Lai

Thi Kieu Khanh Ho

2024-01-01

ECAI (publié)

Explainable Attention for Few-shot Learning and Beyond

Bahareh Nikpour

2023-10-11

ArXiv (prépublication)

Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review

Lily Puterman-Salzman

Jory Katz

Howard Bergman

Roland Grad

Vladimir Khanassov

Genevieve Gore

Isabelle Vedel

Machelle Wilchesky

Samira Abbasgholizadeh-Rahimi

Negar Ghourchian

Background: Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence … (voir plus)(AI) may help in detection and screening of dementia; however, little is known in this area. Objectives: The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method: The review followed the framework proposed by O’Malley’s and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson’s or Huntington’s disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results: After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions: We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.

2023-09-13

Dementia and Geriatric Cognitive Disorders EXTRA (publié)