Publications

On the Identifiability of Quantized Factors

Vitória Barin Pacela

Kartik Ahuja

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

Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons

Simon Dufort-Labbé

Pierluca D'Oro

Evgenii Nikishin

Razvan Pascanu

Pierre-Luc Bacon

Aristide Baratin

2024-03-12