Publications

Forward-Backward Knowledge Distillation for Continual Clustering
Mohammadreza Sadeghi
Zihan Wang
Narges Armanfard
Unsupervised Continual Learning (UCL) is a burgeoning field in machine learning, focusing on enabling neural networks to sequentially learn … (see more)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.
2024-05-28
ArXiv (preprint)
doi.org
arxiv.org
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization
Jordi Armengol-Estap'e
Vincent Michalski
Ramnath Kumar
Pierre-Luc St-Charles
Doina Precup
S Ebrahimi Kahou
Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that cross… (see more)-modal learning can improve representations for few-shot classification. More specifically, language is a rich modality that can be used to guide visual learning. In this work, we experiment with a multi-modal architecture for few-shot learning that consists of three components: a classifier, an auxiliary network, and a bridge network. While the classifier performs the main classification task, the auxiliary network learns to predict language representations from the same input, and the bridge network transforms high-level features of the auxiliary network into modulation parameters for layers of the few-shot classifier using conditional batch normalization. The bridge should encourage a form of lightweight semantic alignment between language and vision which could be useful for the classifier. However, after evaluating the proposed approach on two popular few-shot classification benchmarks we find that a) the improvements do not reproduce across benchmarks, and b) when they do, the improvements are due to the additional compute and parameters introduced by the bridge network. We contribute insights and recommendations for future work in multi-modal meta-learning, especially when using language representations.
2024-05-28
ArXiv (preprint)
doi.org
arxiv.org
Arbuscular and ectomycorrhizal tree seedling growth is inhibited by competition from neighboring roots and associated fungal hyphae
Vlad Parasquive
Jacques Brisson
Étienne Laliberté
Pierre Luc Chagnon
2024-05-27
Plant and Soil (published)
doi.org
ERS0: Enhancing Military Cybersecurity with AI-Driven SBOM for Firmware Vulnerability Detection and Asset Management
Max Beninger
Philippe Charland
Steven H. H. Ding
Benjamin C. M. Fung
Firmware vulnerability detection and asset management through a software bill of material (SBOM) approach is integral to defensive military … (see more)operations. SBOMs provide a comprehensive list of software components, enabling military organizations to identify vulnerabilities within critical systems, including those controlling various functions in military platforms, as well as in operational technologies and Internet of Things devices. This proactive approach is essential for supply chain security, ensuring that software components are sourced from trusted suppliers and have not been tampered with during production, distribution, or through updates. It is a key element of defense strategies, allowing for rapid assessment, response, and mitigation of vulnerabilities, ultimately safeguarding military capabilities and information from cyber threats. In this paper, we propose ERS0, an SBOM system, driven by artificial intelligence (AI), for detecting firmware vulnerabilities and managing firmware assets. We harness the power of pre-trained large-scale language models to effectively address a wide array of string patterns, extending our coverage to thousands of third-party library patterns. Furthermore, we employ AI-powered code clone search models, enabling a more granular and precise search for vulnerabilities at the binary level, reducing our dependence on string analysis only. Additionally, our AI models extract high-level behavioral functionalities in firmware, such as communication and encryption, allowing us to quantitatively define the behavioral scope of firmware. In preliminary comparative assessments against open-source alternatives, our solution has demonstrated better SBOM coverage, accuracy in vulnerability identification, and a wider array of features.
2024-05-27
2024 16th International Conference on Cyber Conflict: Over the Horizon (CyCon) (published)
doi.org
Structured Learning in Time-dependent Cox Models
Guanbo Wang
Yi Lian
Archer Y. Yang
Robert W. Platt
Rui Wang
Sylvie Perreault
Marc Dorais
Mireille E. Schnitzer
Cox models with time-dependent coefficients and covariates are widely used in survival analysis. In high-dimensional settings, sparse regula… (see more)rization techniques are employed for variable selection, but existing methods for time-dependent Cox models lack flexibility in enforcing specific sparsity patterns (i.e., covariate structures). We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules. Our method can adapt to arbitrary grouping structures, including interaction selection, temporal, spatial, tree, and directed acyclic graph structures. It achieves accurate estimation with low false alarm rates. We develop the sox package, implementing a network flow algorithm for efficiently solving models with complex covariate structures. sox offers a user-friendly interface for specifying grouping structures and delivers fast computation. Through examples, including a case study on identifying predictors of time to all-cause death in atrial fibrillation patients, we demonstrate the practical application of our method with specific selection rules.
2024-05-27
Statistics in Medicine (unknown)
doi.org
arxiv.org
The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity
Prakhar Ganesh
Ihsan Ibrahim Daldaban
Ignacio Cofone
Golnoosh Farnadi
Model multiplicity, the phenomenon where multiple models achieve similar performance despite different underlying learned functions, introdu… (see more)ces arbitrariness in model selection. While this arbitrariness may seem inconsequential in expectation, its impact on individuals can be severe. This paper explores various individual concerns stemming from multiplicity, including the effects of arbitrariness beyond final predictions, disparate arbitrariness for individuals belonging to protected groups, and the challenges associated with the arbitrariness of a single algorithmic system creating a monopoly across various contexts. It provides both an empirical examination of these concerns and a comprehensive analysis from the legal standpoint, addressing how these issues are perceived in the anti-discrimination law in Canada. We conclude the discussion with technical challenges in the current landscape of model multiplicity to meet legal requirements and the legal gap between current law and the implications of arbitrariness in model selection, highlighting relevant future research directions for both disciplines.
2024-05-27
ArXiv (preprint)
doi.org
arxiv.org
Towards a Reliable French Speech Recognition Tool for an Automated Diagnosis of Learning Disabilities
Jihene Rezgui
Félix Jobin
Younes Kechout
Chritine Turgeon
Foutse Khomh
Dyslexia, characterized by severe challenges in reading and spelling acquisition, presents a substantial barrier to proficient literacy, res… (see more)ulting in significantly reduced reading speed (2 to 3 times slower) and diminished text comprehension. With a prevalence ranging from 5G to 10% in the population, early intervention by speech and language pathologists (SLPs) can mitigate dyslexia's effects, but the diagnosis bottleneck impedes timely support. To address this, we propose leveraging machine learning tools to expedite the diagnosis process, focusing on automating phonetic transcription, a critical step in dyslexia assessment. We investigated the practicality of two model configurations utilizing Google's speech-to-text API with children speech in evaluation scenarios and compared their results against transcriptions crafted by experts. The first configuration focuses on Google API's speech-to-text while the second integrates Phonemizer, a text-to-phonemes tool based on a dictionary. Results analysis indicate that our Google-Phonemizer model yields reading accuracies comparable to those computed from human-made transcriptions, offering promise for clinical application. These findings underscore the potential of AI-driven solutions to enhance dyslexia diagnosis efficiency, paving the way for improved accessibility to vital SLP services.
2024-05-27
2024 International Conference on Smart Applications, Communications and Networking (SmartNets) (published)
doi.org
Advancing Cultural Inclusivity: Optimizing Embedding Spaces for Balanced Music Recommendations
Armin Moradi
Nicola Neophytou
Golnoosh Farnadi
2024-05-26
ArXiv (preprint)
doi.org
arxiv.org
Comparative Study of Large Language Model Architectures on Frontier
Junqi Yin
Avishek Bose
Guojing Cong
Isaac Lyngaas
Quentin Gregory Anthony
Large language models (LLMs) have garnered significant attention in both the AI community and beyond. Among these, the Generative Pre-traine… (see more)d Transformer (GPT) has emerged as the dominant architecture, spawning numerous variants. However, these variants have undergone pre-training under diverse conditions, including variations in input data, data preprocessing, and training methodologies, resulting in a lack of controlled comparative studies. Here we meticulously examine two prominent open-sourced GPT architectures, GPT-NeoX and LLaMA, leveraging the computational power of Frontier, the world’s first Exascale supercomputer. Employing the same materials science text corpus and a comprehensive end-to-end pipeline, we conduct a comparative analysis of their training and downstream performance. Our efforts culminate in achieving state-of-the-art performance on a challenging materials science benchmark. Furthermore, we investigate the computation and energy efficiency, and propose a computationally efficient method for architecture design. To our knowledge, these pre-trained models represent the largest available for materials science. Our findings provide practical guidance for building LLMs on HPC platforms.
2024-05-26
2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (published)
doi.org
arxiv.org
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport
Muhammad Haseeb Aslam
Muhammad Osama Zeeshan
Soufiane Belharbi
Marco Pedersoli
Alessandro Lameiras Koerich
Simon Bacon
Eric Granger
Deep learning models for multimodal expression recognition have reached remarkable performance in controlled laboratory environments because… (see more) of their ability to learn complementary and redundant semantic information. However, these models struggle in the wild, mainly because of the unavailability and quality of modalities used for training. In practice, only a subset of the training-time modalities may be available at test time. Learning with privileged information enables models to exploit data from additional modalities that are only available during training. State-of-the-art knowledge distillation (KD) methods have been proposed to distill information from multiple teacher models (each trained on a modality) to a common student model. These privileged KD methods typically utilize point-to-point matching, yet have no explicit mechanism to capture the structural information in the teacher representation space formed by introducing the privileged modality. We argue that encoding this same structure in the student space may lead to enhanced student performance. This paper introduces a new structural KD mechanism based on optimal transport (OT), where entropy-regularized OT distills the structural dark knowledge. Our privileged KD with OT (PKDOT) method captures the local structures in the multimodal teacher representation by calculating a cosine similarity matrix and selecting the top-k anchors to allow for sparse OT solutions, resulting in a more stable distillation process. Experiments1 were performed on two challenging problems - pain estimation on the Biovid dataset (ordinal classification) and arousal-valance prediction on the Affwild2 dataset (regression). Results show that our proposed method can outperform state-of-the-art privileged KD methods on these problems. The diversity among modalities and fusion architectures indicates that PKDOT is modality-and model-agnostic.
2024-05-26
2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG) (published)
doi.org
arxiv.org
Estimating Expectations without Sampling: Neural Stein Estimation
Mohsin Hasan
Dinghuai Zhang
Cheikh Ahmed
Awa Khouna
Yoshua Bengio
We propose a method for estimating the expected value of a given function …
2024-05-26
approximateinference.org/AABI/2024/Symposium (accepted)
openreview.net
openreview.net
Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
Soufiane Belharbi
Marco Pedersoli
Alessandro Lameiras Koerich
Simon Bacon
Eric Granger
Although state-of-the-art classifiers for facial expression recognition (FER) can achieve a high level of accuracy, they lack interpretabili… (see more)ty, an important feature for end-users. Experts typically associate spatial action units (AUs) from a codebook to facial regions for the visual interpretation of expressions. In this paper, the same expert steps are followed. A new learning strategy is proposed to explicitly incorporate AU cues into classifier training, allowing to train deep interpretable models. During training, this AU codebook is used, along with the input image expression label, and facial landmarks, to construct a AU heatmap that indicates the most discriminative image regions of interest w.r.t the facial expression. This valuable spatial cue is leveraged to train a deep interpretable classifier for FER. This is achieved by constraining the spatial layer features of a classifier to be correlated with AU heatmaps. Using a composite loss, the classifier is trained to correctly classify an image while yielding interpretable visual layer-wise attention correlated with AU maps, simulating the expert decision process. Our strategy only relies on image class expression for supervision, without additional manual annotations. Our new strategy is generic, and can be applied to any deep CNN - or transformer-based classifier without requiring any architectural change or significant additional training time. Our extensive evaluation11Our code is available at:https://github.com/sbelharbi/interpretable-fer-aus. on two public benchmarks RAF-DB, and AffectNet datasets shows that our proposed strategy can improve layer-wise interpretability without degrading classification performance. In addition, we explore a common type of interpretable classifiers that rely on class activation mapping (CAM) methods, and show that our approach can also improve CAM interpretability.
2024-05-26
2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG) (published)
doi.org
arxiv.org