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Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a…

Machine Learning · Computer Science 2021-03-30 Yu Zhang , Qiang Yang

Machine learning (ML) has seen tremendous advancements, but its environmental footprint remains a concern. Acknowledging the growing environmental impact of ML this paper investigates Green ML, examining various model architectures and…

Machine Learning · Computer Science 2024-06-21 Ioannis Mavromatis , Kostas Katsaros , Aftab Khan

In the past few years, the area of Machine Learning (ML) has witnessed tremendous advancements, becoming a pervasive technology in a wide range of applications. One area that can significantly benefit from the use of ML is Combinatorial…

Artificial Intelligence · Computer Science 2018-07-17 Michele Lombardi , Michela Milano

The field of machine learning (ML) has gained widespread adoption, leading to significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial. The predominant approaches towards the automation of solving ML…

Machine Learning · Computer Science 2024-02-20 Lei Zhang , Yuge Zhang , Kan Ren , Dongsheng Li , Yuqing Yang

Machine learning enables the extraction of useful information from large, diverse datasets. However, despite many successful applications, machine learning continues to suffer from performance and transparency issues. These challenges can…

Machine Learning · Computer Science 2025-07-08 V. C. Storey , J. Parsons , A. Castellanos , M. Tremblay , R. Lukyanenko , W. Maass , A. Castillo

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems…

Machine Learning · Computer Science 2020-06-04 Jan Bosch , Ivica Crnkovic , Helena Holmström Olsson

Machine learning models are being used extensively in many important areas, but there is no guarantee a model will always perform well or as its developers intended. Understanding the correctness of a model is crucial to prevent potential…

Machine Learning · Computer Science 2021-04-13 Huong Ha , Sunil Gupta , Santu Rana , Svetha Venkatesh

With the rapid integration of Machine Learning (ML) in business applications and processes, it is crucial to ensure the quality, reliability and reproducibility of such systems. We suggest a methodical approach towards ML system quality…

Machine Learning · Computer Science 2025-02-26 Angelantonio Castelli , Georgios Christos Chouliaras , Dmitri Goldenberg

The reliability of machine learning (ML) software systems is heavily influenced by changes in data over time. For that reason, ML systems require regular maintenance, typically based on model retraining. However, retraining requires…

Machine Learning · Computer Science 2025-06-18 Lorena Poenaru-Olaru , June Sallou , Luis Cruz , Jan Rellermeyer , Arie van Deursen

Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks,…

Machine Learning · Computer Science 2022-08-10 Zhi-Hua Zhou

Machine learning (ML) techniques are increasingly applied to decision-making and control problems in Cyber-Physical Systems among which many are safety-critical, e.g., chemical plants, robotics, autonomous vehicles. Despite the significant…

Systems and Control · Electrical Eng. & Systems 2019-09-12 Xiaozhe Gu , Arvind Easwaran

Any traditional engineering field has metrics to rigorously assess the quality of their products. Engineers know that the output must satisfy the requirements, must comply with the production and market rules, and must be competitive.…

Operations is a key challenge in the domain of machine learning pipeline deployments involving monitoring and management of real-time prediction quality. Typically, metrics like accuracy, RMSE etc., are used to track the performance of…

Traditional metrics like accuracy, F1-score, and precision are frequently used to evaluate machine learning models, however they may not be sufficient for evaluating performance on tiny, unbalanced, or high-dimensional datasets. A…

Machine Learning · Computer Science 2024-12-11 Serzhan Ossenov

Machine Learning (ML) and its applications have been transforming our lives but it is also creating issues related to the development of fair, accountable, transparent, and ethical Artificial Intelligence. As the ML models are not fully…

Applications · Statistics 2021-06-30 Yihuang Kang , Yi-Wen Chiu , Ming-Yen Lin , Fang-yi Su , Sheng-Tai Huang

State-of-the-art machine learning algorithms demonstrate close to absolute performance in selected challenges. We provide arguments that the reason can be in low variability of the samples and high effectiveness in learning typical…

Computer Vision and Pattern Recognition · Computer Science 2018-07-25 Egor Illarionov , Roman Khudorozhkov

Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline…

Machine Learning · Computer Science 2024-04-15 Lanpei Li , Elia Piccoli , Andrea Cossu , Davide Bacciu , Vincenzo Lomonaco

Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data mining algorithms, and usually require large amount of label…

Machine Learning · Statistics 2018-11-13 Yong Luo , Yonggang Wen , Ling-Yu Duan , Dacheng Tao

There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers…

Machine Learning · Computer Science 2020-05-05 Anh Truong , Austin Walters , Jeremy Goodsitt , Keegan Hines , C. Bayan Bruss , Reza Farivar

Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on…

Machine Learning · Statistics 2024-06-04 David W. Hogg , Soledad Villar