Massachusetts Institute of Technology（麻省理工学院）；University of Toronto（多伦多大学）；Evidation Health, Inc.（Evidation健康公司）；New York University（纽约大学）；Vector Institute（向量研究所）
Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini
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Presented at the ICLR 2019 Reproducibility in Machine Learning Workshop
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning.
In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility. We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data and code accessibility. Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
McGill University（麦吉尔大学）；Facebook AI Research（Facebook人工智能研究院）；CIFAR（加拿大高等研究院）；Université de Montréal（蒙特利尔大学）；Yahoo! Research（雅虎研究院）；Télécom Paris（巴黎电信学院）；Institut Polytechnique de France（法国理工学院）；University of Washington（华盛顿大学）；Apple（苹果公司）；Google（谷歌）
Reproducibility in machine learning for medical imaging
Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑科学院）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗救助机构）；Hôpital de la Pitié-Salpêtrière（巴黎硝石库慈善医院）；University of Twente（特文特大学）
Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles
Heinz-Nixdorf Chair for Distributed Information Systems（海因茨-尼克斯多夫分布式信息系统讲席）；Michael Stifel Center Jena（耶拿米夏埃尔·施蒂费尔中心）；Friedrich Schiller University Jena（耶拿弗里德里希·席勒大学）
The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability
University of Bonn（波恩大学）；Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS)（弗劳恩霍夫智能分析与信息系统研究所（IAIS））；Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)（弗劳恩霍夫算法与科学计算研究所（SCAI））
在连续向量空间中嵌入知识图谱（KG）以便将其用于机器学习任务的新兴趋势。最近，已经提出了许多知识图谱嵌入（KGE）模型，其在学习低维表示的同时试图保持KGs的结构属性，诸如节点的相似性取决于它们的边缘到其他节点。KGE可以用于解决KG内的任务，例如预测新链接和消除实体的歧义。它们也可以用于下游任务，如问答和事实核查。总的来说，这些任务与语义Web社区相关。尽管它们很受欢迎，但KGE实验的可重复性和所提出的KGE模型到机器学习社区以外的研究领域的可转移性可能是一个重大挑战。因此，我们提出了KEEN Universe，这是一个知识图谱嵌入的生态系统，我们开发了该生态系统，重点关注可重复性和可转移性。KEEN Universe目前由Python包PyKEEN（Python KnowlEdge EmbeddiNgs）、BioKEEN（Biological KnowlEdge EmbeddiNgs）和KEEN Model Zoo组成，用于与社区共享经过训练的KGE模型。
Reproducibility Assessment of Magnetic Resonance Spectroscopy of Pregenual Anterior Cingulate Cortex across Sessions and Vendors via the Cloud Computing Platform CloudBrain-MRS
National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康与医学数据科学国家研究院）；College of Ocean & Earth Sciences, Xiamen University（厦门大学海洋与地球学院）；Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University（厦门大学福建省等离子体与磁共振重点实验室）；Department of Electronic Science, Xiamen University（厦门大学电子科学系）；Clinical and Technical Support, Philips Healthcare（飞利浦医疗保健临床与技术支持部）；School of Economics and Management, Xiamen University of Technology（厦门理工学院经济与管理学院）；Department of Radiology, the First Hospital Affiliated to Xiamen University（厦门大学附属第一医院放射科）；China Mobile (Suzhou) Software Technology Company Limited（中国移动苏州软件技术有限公司）；School of Computer and Information Engineering, Xiamen University of Technology（厦门理工学院计算机与信息工程学院）
Massachusetts Institute of Technology（麻省理工学院）；Vector Institute（向量研究所）；University of Toronto（多伦多大学）；New York University（纽约大学）；New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）
A Review of Challenges and Opportunities in Machine Learning for Health
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Microsoft Research（微软研究院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Harvard School of Public Health（哈佛公共卫生学院）；Massachusetts Institute of Technology（麻省理工学院）；New York University（纽约大学）
Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
What is Fair? Defining Fairness in Machine Learning for Health
Department of Statistical Sciences, University of Toronto（统计科学系，多伦多大学）；Harvard T.H. Chan School of Public Health（哈佛T.H. Chan公共卫生学院）；York University（约克大学）；Verily Life Sciences（Verily生命科学公司）；Duke University（杜克大学）；University of Toronto（多伦多大学）
Massachusetts Institute of Technology（麻省理工学院）；Microsoft Research（微软研究院）；Stanford University（斯坦福大学）；Vector Institute（向量研究院）；New York University（纽约大学）；University of Toronto（多伦多大学）
Physics-informed data-driven machine health monitoring for two-photon lithography
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, United States（密歇根大学安娜堡分校机械工程系）；Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States（伊利诺伊大学厄巴纳-香槟分校机械科学与工程系）
Evaluating the performance of personal, social, health-related, biomarker and genetic data for predicting an individuals future health using machine learning: A longitudinal analysis
Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
Healthy Lifespan Institute, School of Computer Science, University of Sheffield（健康寿命研究所，计算机科学学院，谢菲尔德大学）；School of Electrical and Electronic Engineering, University of Sheffield（电子与电气工程学院，谢菲尔德大学）；Healthy Lifespan Institute, School of Sociological Studies, Politics and International Relations, University of Sheffield（健康寿命研究所，社会科学学院，政治与国际关系，谢菲尔德大学）；Digital Environment Research Institute, Queen Mary University of London（数字环境研究 institutes，伦敦女王大学）
