Publications

Reinforcement learning for freight booking control problems
Justin Dumouchelle
Emma Frejinger
Andrea Lodi
2024-03-16
Journal of Revenue and Pricing Management (publié)
doi.org
arxiv.org
Normalizing Spinal Cord Compression Morphometric Measures: Application in Degenerative Cervical Myelopathy
Sandrine Bédard
Jan Valošek
Maryam Seif PhD
Armin Curt PhD
Simon Schading Md
M.Sc
Nikolai Pfender
Patrick Freund Md
Markus Hupp MD PhD
Julien Cohen-adad Md
Objective: Automatic and robust characterization of spinal cord shape from MRI images is relevant to assess the severity of spinal cord comp… (voir plus)ression in degenerative cervical myelopathy (DCM) and to guide therapeutic strategy. Despite its popularity, the maximum spinal cord compression (MSCC) index has practical limitations to objectively assess the severity of cord compression. Firstly, it is computed by normalizing the anteroposterior cord diameter by that above and below the level of compression, but it does not account for the fact that the spinal cord itself varies in size along the superior-inferior axis, making this MSCC sensitive to the level of compression. Secondly, spinal cord shape varies across individuals, making MSCC also sensitive to the size and shape of every individual. Thirdly, MSCC is typically computed by the expert-rater on a single sagittal slice, which is time-consuming and prone to inter-rater variability. In this study, we propose a fully automatic pipeline to compute MSCC. Methods: We extended the traditional MSCC (based on the anteroposterior diameter) to other shape metrics (transverse diameter, area, eccentricity, and solidity), and proposed a normalization strategy using a database of healthy adults (n=203) to address the variability of the spinal cord anatomy between individuals. We validated the proposed method in a cohort of DCM patients (n=120) with manually derived morphometric measures and predicted the therapeutic decision (operative/conservative) using a stepwise binary logistic regression including demographics, clinical scores, and electrophysiological assessment. Results: The automatic and normalized MSCC measures significantly correlated with clinical scores and predicted the therapeutic decision with higher accuracy than the manual MSCC. Results show that the sensory dysfunction of the upper extremities (mJOA subscore), the presence of myelopathy and the proposed MRI-based normalized morphometric measures were significant predictors of the therapeutic decision. The model yielded an area under the curve of the receiver operating characteristic of 80%. Conclusion: The study introduced an automatic method for computation of normalized MSCC measures of cord compression from MRI scans, which is an important step towards better informed therapeutic decisions in DCM patients. The method is open-source and available in the Spinal Cord Toolbox v6.0.
2024-03-15
medRxiv (prépublication)
doi.org
Safety Cases: How to Justify the Safety of Advanced AI Systems
Joshua Clymer
Nick Gabrieli
David Scott Krueger
Thomas Larsen
As AI systems become more advanced, companies and regulators will make difficult decisions about whether it is safe to train and deploy them… (voir plus). To prepare for these decisions, we investigate how developers could make a 'safety case,' which is a structured rationale that AI systems are unlikely to cause a catastrophe. We propose a framework for organizing a safety case and discuss four categories of arguments to justify safety: total inability to cause a catastrophe, sufficiently strong control measures, trustworthiness despite capability to cause harm, and -- if AI systems become much more powerful -- deference to credible AI advisors. We evaluate concrete examples of arguments in each category and outline how arguments could be combined to justify that AI systems are safe to deploy.
2024-03-15
ArXiv (prépublication)
doi.org
arxiv.org
On the Identifiability of Quantized Factors
Vitória Barin Pacela
Kartik Ahuja
Simon Lacoste-Julien
Pascal Vincent
Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the… (voir plus) theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.
2024-03-15
Proceedings of the Third Conference on Causal Learning and Reasoning (publié)
proceedings.mlr.press
arxiv.org
Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy
Francisco Berumen
Samuel Ouellet
Shirin A. Enger
Luc Beaulieu
2024-03-14
Physics in Medicine and Biology (publié)
doi.org
Analyzing Data Augmentation for Medical Images: A Case Study in Ultrasound Images
Adam Tupper
Christian Gagné
Data augmentation is one of the most effective techniques to improve the generalization performance of deep neural networks. Yet, despite of… (voir plus)ten facing limited data availability in medical image analysis, it is frequently underutilized. This appears to be due to a gap in our collective understanding of the efficacy of different augmentation techniques across medical imaging tasks and modalities. One domain where this is especially true is breast ultrasound images. This work addresses this issue by analyzing the effectiveness of different augmentation techniques for the classification of breast lesions in ultrasound images. We assess the generalizability of our findings across several datasets, demonstrate that certain augmentations are far more effective than others, and show that their usage leads to significant performance gains.
2024-03-14
ArXiv (prépublication)
doi.org
arxiv.org
Bugs in Large Language Models Generated Code: An Empirical Study
Florian Tambon
Arghavan Moradi Dakhel
Amin Nikanjam
Foutse Khomh
Michel C. Desmarais
Giuliano Antoniol
2024-03-13
ArXiv (prépublication)
doi.org
arxiv.org
Online Bayesian optimization of vagus nerve stimulation.
Lorenz Wernisch
Tristan Edwards
Antonin Berthon
Olivier Tessier-Lariviere
Elvijs Sarkans
Myrta Stoukidi
Pascal Fortier-Poisson
Max Pinkney
Michael Thornton
Catherine Hanley
Susannah Lee
Joel Jennings
Ben Appleton
Philip Garsed
Bret Patterson
Buttinger Will
Samuel Gonshaw
Matjaž Jakopec
Sudhakaran Shunmugam
Jorin Mamen … (voir 4 de plus)
Aleksi Tukiainen
Guillaume Lajoie
Oliver Armitage
Emil Hewage
OBJECTIVE In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimu… (voir plus)lation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging. Comprehensive parameter searching for an optimal stimulation effect is often infeasible in a clinical setting due to the size of the parameter space. Restricting this space, however, may lead to suboptimal therapeutic results, reduced responder rates, and adverse effects. Approach. As an alternative to a full parameter search, we present a flexible machine learning, data acquisition, and processing framework for optimizing neural stimulation parameters, requiring as few steps as possible using Bayesian optimization. This optimization builds a model of the neural and physiological responses to stimulations, enabling it to optimize stimulation parameters and provide estimates of the accuracy of the response model. The vagus nerve innervates, among other thoracic and visceral organs, the heart, thus controlling heart rate, making it an ideal candidate for demonstrating the effectiveness of our approach. Main results. The efficacy of our optimization approach was first evaluated on simulated neural responses, then applied to vagus nerve stimulation intraoperatively in porcine subjects. Optimization converged quickly on parameters achieving target heart rates and optimizing neural B-fiber activations despite high intersubject variability. Significance. An optimized stimulation waveform was achieved in real time with far fewer stimulations than required by alternative optimization strategies, thus minimizing exposure to side effects. Uncertainty estimates helped avoiding stimulations outside a safe range. Our approach shows that a complex set of neural stimulation parameters can be optimized in real-time for a patient to achieve a personalized precision dosing. .
2024-03-13
Journal of Neural Engineering (publié)
doi.org
Scattered Mixture-of-Experts Implementation
Shawn Tan
Yikang Shen
Rameswar Panda
Aaron Courville
We present ScatterMoE, an implementation of Sparse Mixture-of-Experts (SMoE) on GPUs. ScatterMoE builds upon existing implementations, and o… (voir plus)vercoming some of the limitations to improve inference and training speed, and memory footprint. This implementation achieves this by avoiding padding and making excessive copies of the input. We introduce ParallelLinear, the main component we use to build our implementation and the various kernels used to speed up the operation. We benchmark our implementation against Megablocks, and show that it enables a higher throughput and lower memory footprint. We also show how ParallelLinear enables extension of the Mixture-of-Experts concept by demonstrating with an implementation of Mixture of Attention.
2024-03-13
ArXiv (prépublication)
doi.org
arxiv.org
Simple and Scalable Strategies to Continually Pre-train Large Language Models
Adam Ibrahim
Benjamin Thérien
Kshitij Gupta
Mats Leon Richter
Quentin Anthony
Timothee LESORT
Eugene Belilovsky
Irina Rish
Large language models (LLMs) are routinely pre-trained on billions of tokens, only to start the process over again once new data becomes ava… (voir plus)ilable. A much more efficient solution is to continually pre-train these models, saving significant compute compared to re-training. However, the distribution shift induced by new data typically results in degraded performance on previous data or poor adaptation to the new data. In this work, we show that a simple and scalable combination of learning rate (LR) re-warming, LR re-decaying, and replay of previous data is sufficient to match the performance of fully re-training from scratch on all available data, as measured by the final loss and the average score on several language model (LM) evaluation benchmarks. Specifically, we show this for a weak but realistic distribution shift between two commonly used LLM pre-training datasets (English
2024-03-13
ArXiv (prépublication)
doi.org
arxiv.org
Assessing the Security of GitHub Copilot Generated Code - A Targeted Replication Study
Vahid Majdinasab
Michael Joshua Bishop
Shawn Rasheed
Arghavan Moradi Dakhel
Amjed Tahir
Foutse Khomh
2024-03-12
2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (publié)
doi.org
arxiv.org
Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends
Mina Taraghi
Gianolli Dorcelus
Armstrong Foundjem
Florian Tambon
Foutse Khomh
The ubiquity of large-scale Pre-Trained Models (PTMs) is on the rise, sparking interest in model hubs, and dedicated platforms for hosting P… (voir plus)TMs. Despite this trend, a comprehensive exploration of the challenges that users encounter and how the community leverages PTMs remains lacking. To address this gap, we conducted an extensive mixed-methods empirical study by focusing on discussion forums and the model hub of HuggingFace, the largest public model hub. Based on our qualitative analysis, we present a taxonomy of the challenges and benefits associated with PTM reuse within this community. We then conduct a quantitative study to track model-type trends and model documentation evolution over time. Our findings highlight prevalent challenges such as limited guidance for beginner users, struggles with model output comprehensibility in training or inference, and a lack of model understanding. We also identified interesting trends among models where some models maintain high upload rates despite a decline in topics related to them. Additionally, we found that despite the introduction of model documentation tools, its quantity has not increased over time, leading to difficulties in model comprehension and selection among users. Our study sheds light on new challenges in reusing PTMs that were not reported before and we provide recommendations for various stakeholders involved in PTM reuse.
2024-03-12
2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (publié)
doi.org
arxiv.org