Publications

A Framework for Obtaining Accurate Posteriors of Strong Gravitational Lensing Parameters with Flexible Priors and Implicit Likelihoods using Density Estimation
Ronan Legin
Yashar Hezaveh
Laurence Perreault-Levasseur
Benjamin Wandelt
We report the application of implicit likelihood inference to the prediction of the macro-parameters of strong lensing systems with neural n… (voir plus)etworks. This allows us to perform deep learning analysis of lensing systems within a well-defined Bayesian statistical framework to explicitly impose desired priors on lensing variables, to obtain accurate posteriors, and to guarantee convergence to the optimal posterior in the limit of perfect performance. We train neural networks to perform a regression task to produce point estimates of lensing parameters. We then interpret these estimates as compressed statistics in our inference setup and model their likelihood function using mixture density networks. We compare our results with those of approximate Bayesian neural networks, discuss their significance, and point to future directions. Based on a test set of 100,000 strong lensing simulations, our amortized model produces accurate posteriors for any arbitrary confidence interval, with a maximum percentage deviation of
2023-01-18
The Astrophysical Journal (publié)
doi.org
arxiv.org
Label fusion and training methods for reliable representation of inter-rater uncertainty
Andréanne Lemay
Charley Gros
Julien Cohen-Adad
Enamundram Naga Karthik
Medical tasks are prone to inter-rater variability due to multiple factors such as image quality, professional experience and training, or g… (voir plus)uideline clarity. Training deep learning networks with annotations from multiple raters is a common practice that mitigates the model's bias towards a single expert. Reliable models generating calibrated outputs and reflecting the inter-rater disagreement are key to the integration of artificial intelligence in clinical practice. Various methods exist to take into account different expert labels. We focus on comparing three label fusion methods: STAPLE, average of the rater's segmentation, and random sampling of each rater's segmentation during training. Each label fusion method is studied using both the conventional training framework and the recently published SoftSeg framework that limits information loss by treating the segmentation task as a regression. Our results, across 10 data splittings on two public datasets, indicate that SoftSeg models, regardless of the ground truth fusion method, had better calibration and preservation of the inter-rater rater variability compared with their conventional counterparts without impacting the segmentation performance. Conventional models, i.e., trained with a Dice loss, with binary inputs, and sigmoid/softmax final activate, were overconfident and underestimated the uncertainty associated with inter-rater variability. Conversely, fusing labels by averaging with the SoftSeg framework led to underconfident outputs and overestimation of the rater disagreement. In terms of segmentation performance, the best label fusion method was different for the two datasets studied, indicating this parameter might be task-dependent. However, SoftSeg had segmentation performance systematically superior or equal to the conventionally trained models and had the best calibration and preservation of the inter-rater variability.
2023-01-17
Machine Learning for Biomedical Imaging (publié)
doi.org
arxiv.org
Estimating causal effects with optimization-based methods: A review and empirical comparison
Martin Cousineau
Vedat Verter
Susan A. Murphy
Joelle Pineau
2023-01-15
European journal of operational research (publié)
doi.org
arxiv.org
Scalable Neural Network Algorithms for High Dimensional Data
Mukesh Soni
Marwan Ali Shnan
Yoshua Bengio
2023-01-14
Mesopotamian Journal of Big Data (publié)
doi.org
Publisher Correction: Advancing ethics review practices in AI research
Madhulika Srikumar
Rebecca Finlay
Grace M. Abuhamad
Carolyn Ashurst
Rosie Campbell
Emily Campbell-Ratcliffe
Hudson Hongo
Sara Rene Jordan
Joseph Lindley
Aviv Ovadya
Joelle Pineau
2023-01-10
Nature Machine Intelligence (publié)
doi.org
Studying Logging Practice in Machine Learning-based Applications
Patrick Loic Foalem
Foutse Khomh
Heng Li
Logging is a common practice in traditional software development. Several research works have been done to investigate the different charact… (voir plus)eristics of logging practices in traditional software systems (e.g., Android applications, JAVA applications, C/C++ applications). Nowadays, we are witnessing more and more development of Machine Learning-based applications (ML-based applications). Today, there are many popular libraries that facilitate and contribute to the development of such applications, among which we can mention: Pytorch, Tensorflow, Theano, MXNet, Scikit-Learn, Caffe, and Keras. Despite the popularity of ML, we don't have a clear understanding of logging practices in ML applications. In this paper, we aim to fill this knowledge gap and help ML practitioners understand the characteristics of logging in ML-based applications. In particular, we conduct an empirical study on 110 open-source ML-based applications. Through a quantitative analysis, we find that logging practice in ML-based applications is less pervasive than in traditional applications including Android, JAVA, and C/C++ applications. Furthermore, the majority of logging statements in ML-based applications are in info and warn levels, compared to traditional applications where info is the majority of logging statement in C/C++ application and debug, error levels constitute the majority of logging statement in Android application. We also perform a quantitative and qualitative analysis of a random sample of logging statements to understand where ML developers put most of logging statements and examine why and how they are using logging. These analyses led to the following observations: (i) ML developers put most of the logging statements in model training, and in non-ML components. (ii) Data and model management appear to be the main reason behind the introduction of logging statements in ML-based applications.
2023-01-09
ArXiv (prépublication)
doi.org
arxiv.org
SantaCoder: don't reach for the stars!
Loubna Ben allal
Raymond Li
Denis Kocetkov
Chenghao Mou
Christopher Akiki
Carlos Muñoz Ferrandis
Niklas Muennighoff
Mayank Mishra
Alex Gu
Manan Dey
Logesh Kumar Umapathi
Carolyn Jane Anderson
Yangtian Zi
Joel Lamy Poirier
Hailey Schoelkopf
S. Troshin
Dmitry Abulkhanov
Manuel L. Romero
M. Lappert
Francesco De Toni … (voir 21 de plus)
Bernardo Garc'ia del R'io
Qian Liu
Shamik Bose
Urvashi Bhattacharyya
Terry Yue Zhuo
Ian Yu
Paulo Villegas
Marco Zocca
Sourab Mangrulkar
D. Lansky
Huu Nguyen
Danish Contractor
Luisa Villa
Jia LI
Dzmitry Bahdanau
Yacine Jernite
Sean Christopher Hughes
Daniel Fried
Arjun Guha
Harm de Vries
Leandro Von Werra
The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech … (voir plus)report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experiments conducted to de-risk the model architecture, and the experiments investigating better preprocessing methods for the training data. We train 1.1B parameter models on the Java, JavaScript, and Python subsets of The Stack and evaluate them on the MultiPL-E text-to-code benchmark. We find that more aggressive filtering of near-duplicates can further boost performance and, surprisingly, that selecting files from repositories with 5+ GitHub stars deteriorates performance significantly. Our best model outperforms previous open-source multilingual code generation models (InCoder-6.7B and CodeGen-Multi-2.7B) in both left-to-right generation and infilling on the Java, JavaScript, and Python portions of MultiPL-E, despite being a substantially smaller model. All models are released under an OpenRAIL license at https://hf.co/bigcode.
2023-01-08
ArXiv (prépublication)
doi.org
arxiv.org
The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions
Sonia Gazeau
Xiaoyan Deng
Hsu Kiang Ooi
Fatima Mostefai
Julie G Hussin
Jane Heffernan
Adrianne L. Jenner
Morgan Craig
2023-01-07
Immunoinformatics (Amsterdam, Netherlands) (publié)
doi.org
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design
Houssem Ben Braiek
Ali Tfaily
Foutse Khomh
Thomas Reid
Ciro Guida
2023-01-04
Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (publié)
doi.org
arxiv.org
A Tweedie Compound Poisson Model in Reproducing Kernel Hilbert Space
Yi Lian
Archer Yang
Boxiang Wang
Peng Shi
Robert William Platt
Abstract Tweedie models can be used to analyze nonnegative continuous data with a probability mass at zero. There have been wide application… (voir plus)s in natural science, healthcare research, actuarial science, and other fields. The performance of existing Tweedie models can be limited on today’s complex data problems with challenging characteristics such as nonlinear effects, high-order interactions, high-dimensionality and sparsity. In this article, we propose a kernel Tweedie model, Ktweedie, and its sparse variant, SKtweedie, that can simultaneously address the above challenges. Specifically, nonlinear effects and high-order interactions can be flexibly represented through a wide range of kernel functions, which is fully learned from the data; In addition, while the Ktweedie can handle high-dimensional data, the SKtweedie with integrated variable selection can further improve the interpretability. We perform extensive simulation studies to justify the prediction and variable selection accuracy of our method, and demonstrate the applications in ratemaking and loss-reserving in general insurance. Overall, the Ktweedie and SKtweedie outperform existing Tweedie models when there exist nonlinear effects and high-order interactions, particularly when the dimensionality is high relative to the sample size. The model is implemented in an efficient and user-friendly R package ktweedie (https://cran.r-project.org/package=ktweedie).
2023-01-02
Technometrics (publié)
doi.org
HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
Moein Heidari
Amirhossein Kazerouni
Milad Soltany
Reza Azad
Ehsan Khodapanah Aghdam
Julien Cohen-Adad
Dorit Merhof
Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation … (voir plus)in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contrast, it is demonstrated that both local and global features are crucial for dense prediction, such as segmenting in challenging contexts. In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation. Specifically, we design two multi-scale feature representations using the seminal Swin Transformer module and a CNN-based encoder. To secure a fine fusion of global and local features obtained from the two aforementioned representations, we propose a Double-Level Fusion (DLF) module in the skip connection of the encoder-decoder structure. Extensive experiments on various medical image segmentation datasets demonstrate the effectiveness of HiFormer over other CNN-based, transformer-based, and hybrid methods in terms of computational complexity, and quantitative and qualitative results. Our code is publicly available at: https://github.com/amirhossein-kz/HiFormer
2023-01-01
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)
doi.org
arxiv.org
Hyperspherical Quantization: Toward Smaller and More Accurate Models
Dan Liu
X. T. Chen
Chen Ma
Xue Liu
Model quantization enables the deployment of deep neural networks under resource-constrained devices. Vector quantization aims at reducing t… (voir plus)he model size by indexing model weights with full-precision embeddings, i.e., codewords, while the index needs to be restored to 32-bit during computation. Binary and other low-precision quantization methods can reduce the model size up to 32×, however, at the cost of a considerable accuracy drop. In this paper, we propose an efficient framework for ternary quantization to produce smaller and more accurate compressed models. By integrating hyperspherical learning, pruning and reinitialization, our proposed Hyperspherical Quantization (HQ) method reduces the cosine distance between the full-precision and ternary weights, thus reducing the bias of the straight-through gradient estimator during ternary quantization. Compared with existing work at similar compression levels (~30×, ~40×), our method significantly improves the test accuracy and reduces the model size.
2023-01-01
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)
doi.org
arxiv.org