23 papers · ranked by Valyu relevance
Rob Ashmore, Radu Călinescu, Colin Paterson
Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic and political agendas. Such unprecedented interest is fuelled by a vision of ML…
Marius Schlegel, Kai-Uwe Sattler
The explorative and iterative nature of developing and operating machine learning (ML) applications leads to a variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software, configurations, and logs. In order to enable comparability, reproducibility, and traceability of these artifacts…
Renan Souza, Leonardo Guerreiro Azevedo, Vítor Lourenço, Elton Soares + 9 more
'Elton Soares' 'Raphael Thiago' 'Rafael Brandão' 'Daniel Civitarese' 'Emílio Vital Brazil' 'Márcio Ferreira Moreno' 'Patrick Valduriez' 'Marta Mattoso' 'Renato Cerqueira' 'Marco A. S. Netto'] Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting…
Rama Akkiraju, Vibha Singhal Sinha, Anbang Xu, Jalal Mahmud + 4 more
'Pritam Gundecha' 'Zhe Liu' 'Xiaotong Liu' 'John N. Schumacher'] Abstract—Academic literature on machine learning modeling fails to address how to make machine learning models work for enterprises. For example, existing machine learning processes cannot address how to define business use cases for an AI application…
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic, Siddha Ganju + 11 more
'Siddha Ganju' 'Dava Newman' 'Sujoy Ganguly' 'Danny Lange' 'Atílím Güneş Baydin' 'Amit Sharma' 'Adam Gibson' 'Stephan Zheng' 'Eric P. Xing' 'Chris Mattmann' 'James Parr' 'Yarin Gal'] The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed…
Jaganmohan Chandrasekaran, Tyler Cody, Nicola McCarthy, Erin Lanus + 1 more
'Laura Freeman'] Machine learning (ML) – based software systems are rapidly gaining adoption across various domains, making it increasingly essential to ensure they perform as intended. This report presents best practices for the Test and Evaluation (T&E) of ML-enabled software systems across its lifecycle. We…
Yuanhao Xie, Luís Cruz, Petra Heck, Jan S. Rellermeyer
—The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of…
Adrian H. Zai, Mohammad Adibuzzaman, David D. McManus, Allan Walkey
While the CHAI Blueprint outlines a lifecycle for responsible AI that includes assessment, planning, development, validation, deployment, and monitoring, health systems often require terminology that more directly reflects how interdisciplinary teams conceptualize and execute their work. To support this alignment, we…
Mark Haakman, Luís Cruz, Hennie Huijgens, Arie van Deursen
Tech-leading organizations are embracing the forthcoming artificial intelligence revolution. Intelligent systems are replacing and cooperating with traditional software components. Thus, the same development processes and standards in software engineering ought to be complied in artificial intelligence systems. This…
Piotr Tynecki, Arkadiusz Guziński, Joanna Kazimierczak, Michał Jadczuk + 2 more
As antibiotic resistance is becoming a major problem nowadays in a treatment of infections, bacteriophages (also known as phages) seem to be an alternative. However, to be used in a therapy, their life cycle should be strictly lytic. With the growing popularity of Next Generation Sequencing (NGS) technology, it is…
Feng Feng, Zhenru Chen, Jianyuan Ni, Yuanxun Zhang + 3 more
Drinking water is essential to public health and socioeconomic growth. Therefore, assessing and ensuring drinking water supply is a critical task in modern society. Conventional approaches to analyzing and controlling drinking water quality are labor-intensive and costly with a low throughput. Machine learning (ML) is…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
Shahadat Uddin, Stephen Ong, Haohui Lu
The analytic procedures incorporated to facilitate the delivery of projects are often referred to as project analytics. Existing techniques focus on retrospective reporting and understanding the underlying relationships to make informed decisions. Although machine learning algorithms have been widely used in addressing…
Zhaoyu Zhai, Zhewei Lin, Qiang Li, Jianbo Pan
The explosive growth of numerical biomedical data poses a challenge in uncovering meaningful insights within from vast omics and clinical data. In recent years, machine learning has emerged as a powerful tool for processing and dissecting numerical biomedical data, making it a popular choice for addressing analytical…
Qiang Gu, Anup Kumar, Simon Bray, Allison Creason + 4 more
Supervised machine learning, where the goal is to predict labels of new instances by training on labeled data, has become an essential tool in biomedical data analysis. To make supervised machine learning more accessible to biomedical scientists, we have developed Galaxy-ML, a platform that enables scientists to…
Paulo Lyra, Junhao Qiu, Khai Dang, Alyssa Pybus + 6 more
Machine learning is increasingly central to biomedical research, but using machine learning well often requires substantial computational expertise and methodological care to produce high-quality results. To make machine learning tools more accessible to biomedical researchers while supporting best-practice approaches…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…
Azadeh Assadi, Peter C. Laussen, Andrew J. Goodwin, Sebastian Goodfellow + 9 more
'Sebastian Goodfellow' 'William Dixon' 'Robert W. Greer' 'Anusha Jegatheeswaran' 'Devin Singh' 'Melissa McCradden' 'Sara N. Gallant' 'Anna Goldenberg' 'Danny Eytan' 'Mjaye L. Mazwi'] Background and Objectives Machine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare…
Anubhav Jain
The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials…
Sandra Eloranta, Magnus Boman
The deployment of machine learning for tasks relevant to complementing standard of care and advancing tools for precision health has gained much attention in the clinical community, thus meriting further investigations into its broader use. In an introduction to predictive modelling using machine learning, we conducted…
Authors not listed
Integrating machine learning (ML) into drug discovery has ushered in a new era of innovation, dramatically enhancing the efficiency and precision of identifying and developing new therapeutics. This review provides a comprehensive analysis of the current applications of machine learning in drug discovery, focusing on…
Joshua J. Levy, A. James O’Malley
Machine learning approaches have become increasingly popular modeling techniques, relying on data-driven heuristics to arrive at its solutions. Recent comparisons between these algorithms and traditional statistical modeling techniques have largely ignored the superiority gained by the former approaches due to…