检索结果-内蒙古大学图书馆

Lung Ultrasound for the Detection of Pulmonary Tuberculosis Using Expert- and AI-Guided Interpretation: A Prospective Cohort Study

学校读者我要写书评

暂无评论

SSRN

SSRN 2025年

作者： Suttels, Véronique Brokowski, Trevor Wachinou, Ablo Prudence Wolleb, Julia Hada, Aboudou Rasisou Du Toit, Jacques Daniel Fiogbé, Arnauld Attannon Guendehou, Brice Alovokpinhou, Frederic Sefou, Fadyl Makpemikpa, Ginette Bessat, Cécile Roux, Alexia Garcia, Elena Brahier, Thomas Opota, Onya Doenz, Jonathan Vignoud, Julien Agodokpessi, Gildas Affolabi, Dissou Hartley, Mary-Anne Boillat-Blanco, Noémie Department of Medicine Infectious Diseases Lausanne University Hospital University of Lausanne Lausanne Switzerland Yale School of Medicine Department of Biomedical Informatics & Data Science New HavenCT06510 United States Cotonou Benin Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Emergency Department Lausanne University Hospital University of Lausanne Lausanne1011 Switzerland Institute of Microbiology University of Lausanne University Hospital Centre Lausanne Switzerland Lausanne1015 Switzerland National Reference Laboratory for Mycobacteriology Cotonou Benin Yale University United States Faculty of Health Sciences Benin Intelligent Global Health Machine Learning and Optimization Laboratory

Background: Point-of-care lung ultrasound (LUS) is a promising tool for portable sputum-free tuberculosis (TB) triage. We investigate the diagnostic performance of LUS to detect TB using expert and artificial intelligence (AI) guided interpretation. We introduce ULTR-AI (Ultrasound-led TB recognition using AI), a suite of deep learning (DL) models designed to automate TB prediction from LUS images. Methods: In this prospective cohort study in a tertiary center in urban Benin, a standardized 14-point sliding scan LUS protocol was performed for symptomatic patients by a trained operator. LUS images were reviewed by two blinded and independent readers. Same-day single sputum Xpert MTB/RIF Ultra® was the microbiological reference standard. The suite comprises three AI models, ULTR-AI, ULTR-AI[signs] and ULTR-AI[max]. ULTR-AI predicts TB directly from images using DL, ULTR-AI[signs] first generates human-recognizable pathological signs before TB risk prediction in a machine learning (ML) model. Finally, ULTR-AI[max] takes the maximal TB risk score predicted by these two models. Findings: Out of 760 patients screened, 504 were analyzed. Median age was 40 (IQR 30-52), 196 (39%) were female, 78 (15%) were people with HIV and 66 (13%) had previous TB. Overall, 192 (38%) had bacteriologically confirmed TB. Human expert interpretation of lung ultrasound achieved a sensitivity of 0·90 (95%CI: 0·89, 0·92), specificity of 0·61 (95%CI: 0·54, 0·67) [AUROC 0·84, 95% CI: 0.82-0.85]. AI-guided interpretation with ULTRA-AI[max] reached a sensitivity of 0·91 (95% CI, 0·90–0·96) and a specificity of 0·85 (95% CI, 0·74–0·88) [AUROC 0·93, 95% 0·92-0·95]. Interpretation: In this cohort, AI-guided LUS meets the WHO requirements for a sputum-free TB triage test, enabling point-of-care testing with minimal infrastructure. ULTR-AI could further help decentralize TB diagnostics in LMICs, improving timely detection. Validation in diverse populations is crucial to confirm clinical utility. © 2025,

关键词： Lung cancer

Beyond spectral gap: the role of the topology in decentralized learning

学校读者我要写书评

暂无评论

The Journal of Machine Learning Research

The Journal of machine learning Research 2023年第1期24卷 17074-17104页

作者： Thijs Vogels Hadrien Hendrikx Martin Jaggi Machine Learning and Optimization Laboratory EPFL Lausanne Switzerland

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers communicate over a sparse graph, current theory fails to capture important aspects of real-world behavior. First, the 'spectral gap' of the communication graph is not predictive of its empirical performance in (deep) learning. Second, current theory does not explain that collaboration enables larger learning rates than training alone. In fact, it prescribes smaller learning rates, which further decrease as graphs become larger, failing to explain convergence dynamics in infinite graphs. This paper aims to paint an accurate picture of sparsely-connected distributed optimization. We quantify how the graph topology influences convergence in a quadratic toy problem and provide theoretical results for general smooth and (strongly) convex objectives. Our theory matches empirical observations in deep learning, and accurately describes the relative merits of different graph topologies. This paper is an extension of the conference paper by Vogels et al. (2022). Code: ***/epfml/topology-in-decentralized-learning.

关键词： decentralized learning convex optimization stochastic gradient descent gossip algorithms spectral gap

Expanded Gating Ranges Improve Activation Functions

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Huang, Allen Hao Machine Learning and Optimization Laboratory EPFL Switzerland

Activation functions are core components of all deep learning architectures. Currently, the most popular activation functions are smooth ReLU variants like GELU and SiLU. These are self-gated activation functions where the range of the gating function is between zero and one. In this paper, we explore the viability of using arctan as a gating mechanism. A self-gated activation function that uses arctan as its gating function has a monotonically increasing first derivative. To make this activation function competitive, it is necessary to introduce a trainable parameter for every MLP block to expand the range of the gating function beyond zero and one. We find that this technique also improves existing self-gated activation functions. We conduct an empirical evaluation of Expanded ArcTan Linear Unit (xATLU), Expanded GELU (xGELU), and Expanded SiLU (xSiLU) and show that they outperform existing activation functions within a transformer architecture. Additionally, expanded gating ranges show promising results in improving first-order Gated Linear Units (GLU). © 2024, CC BY.

关键词： Chemical activation

Second-Order optimization with Lazy Hessians

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Doikov, Nikita Chayti, El Mahdi Jaggi, Martin Machine Learning and Optimization Laboratory EPFL Switzerland

We analyze Newton’s method with lazy Hessian updates for solving general possibly non-convex optimization problems. We propose to reuse a previously seen Hessian for several iterations while computing new gradients at each step of the method. This significantly reduces the overall arithmetic complexity of second-order optimization schemes. By using the cubic regularization technique, we establish fast global convergence of our method to a second-order stationary point, while the Hessian does not need to be updated each iteration. For convex problems, we justify global and local superlinear rates for lazy Newton steps with quadratic regularization, which is easier to compute. The optimal frequency for updating the Hessian is once every d iterations, where d is the dimension of the problem. This provably improves the total arithmetic complexity of second-order algorithms by a factor √d. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

Beyond spectral gap (extended): The role of the topology in decentralized learning

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Vogels, Thijs Hendrikx, Hadrien Jaggi, Martin Machine Learning and Optimization Laboratory EPFL Lausanne Switzerland

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers communicate over a sparse graph, current theory fails to capture important aspects of real-world behavior. First, the ‘spectral gap’ of the communication graph is not predictive of its empirical performance in (deep) learning. Second, current theory does not explain that collaboration enables larger learning rates than training alone. In fact, it prescribes smaller learning rates, which further decrease as graphs become larger, failing to explain convergence dynamics in infinite graphs. This paper aims to paint an accurate picture of sparsely-connected distributed optimization. We quantify how the graph topology influences convergence in a quadratic toy problem and provide theoretical results for general smooth and (strongly) convex objectives. Our theory matches empirical observations in deep learning, and accurately describes the relative merits of different graph topologies. This paper is an extension of the conference paper by Vogels et al. (2022). Code: ***/epfml/topology-in-decentralized-learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

Sparsified SGD with memory 18

学校读者我要写书评

暂无评论

Sparsified SGD with memory

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Sebastian U. Stich Jean-Baptiste Cordonnier Martin Jaggi Machine Learning and Optimization Laboratory (MLO) EPFL Switzerland

Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e. algorithms that leverage the compute power of many devices for training. The communication overhead is a key bottleneck that hinders perfect scalability. Various recent works proposed to use quantization or sparsification techniques to reduce the amount of data that needs to be communicated, for instance by only sending the most significant entries of the stochastic gradient (top-k sparsification). Whilst such schemes showed very promising performance in practice, they have eluded theoretical analysis so *** this work we analyze Stochastic Gradient Descent (SGD) with k-sparsification or compression (for instance top-k or random-k) and show that this scheme converges at the same rate as vanilla SGD when equipped with error compensation (keeping track of accumulated errors in memory). That is, communication can be reduced by a factor of the dimension of the problem (sometimes even more) whilst still converging at the same rate. We present numerical experiments to illustrate the theoretical findings and the good scalability for distributed applications.

关键词：

Simple unsupervised keyphrase extraction using sentence embeddings

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Bennani-Smires, Kamil Musat, Claudiu Hossmann, Andreaa Baeriswyl, Michael Jaggi, Martin Data Analytics & AI Swisscom AG Machine Learning and Optimization Laboratory EPFL

关键词： Embeddings

Safe adaptive importance sampling

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Stich, Sebastian U. Raj, Anant Jaggi, Martin EPFL Machine Learning and Optimization Laboratory Max Planck Institute for Intelligent Systems

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants-using importance values defined by the complete gradient information which changes during optimization- enjoy favorable theoretical properties, but are typically computationally infeasible. In this paper we propose an efficient approximation of gradient-based sampling, which is based on safe bounds on the gradient. The proposed sampling distribution is (i) provably the best sampling with respect to the given bounds, (ii) always better than uniform sampling and fixed importance sampling and (iii) can efficiently be computed-in many applications at negligible extra cost. The proposed sampling scheme is generic and can easily be integrated into existing algorithms. In particular, we show that coordinate-descent (CD) and stochastic gradient descent (SGD) can enjoy significant a speed-up under the novel scheme. The proven efficiency of the proposed sampling is verified by extensive numerical testing.90C25, 68W20, 68Q25 Copyright © 2017, The Authors. All rights reserved.

关键词： Importance sampling

Gene locations may contribute to predicting gene regulatory relationships

学校读者我要写书评

暂无评论

Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2018年第1期19卷 25-37页

作者： Jun MENG Wen-yuan XU Xiao CHEN Tao LIN Xiao-yu DENG Department of System Science and Engineering School of Electrical Engineering Zhejiang University Laboratory of Machine Learning and Optimization école Polytechnique Fédérale de Lausanne (EPFL)

We propose that locations of genes on chromosomes can contribute to the prediction of gene regulatory relationships. We constructed a time-based gene regulatory network of zebrafish cardiogenesis on the basis of a spatio-temporal neighborhood method. Through the network, specific regulatory pathways and order of gene expression during zebrafish cardiogenesis were obtained. By comparing the order with locations of these genes on chromosomes, we discovered that there exists a reversal phenomenon between the order and order of gene locations. The discovery provides an inherent rule to instruct exploration of gene regulatory relationships. Specifically, the discovery can help to predict if regulatory relationships between genes exist and contribute to evaluating the correctness of discovered gene regulatory relationships.

关键词： Prediction Gene regulatory relationships Order of gene location Spatio-temporal neighborhood method Zebrafish cardiogenesis