Results 31 to 40 of about 337,942 (263)
, 2020 Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it. Much work has gone towards preventing the default tendency of machine learning models to catastrophically forget, yet virtually all such work involves manually-designed solutions to the ...
Beaulieu, Shawn +6 more
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Adversary Aware Continual Learning
IEEE AccessContinual learning approaches are useful to help a model learn new information or new tasks sequentially, while also retaining the previously acquired information.
Muhammad Umer, Robi Polikar
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Bookworm continual learning: beyond zero-shot learning and continual learning
, 2020 Accepted by TASK-CV workshop at ECCV ...
Wang, Kai +3 more
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Wheat Disease Classification Using Continual Learning
IEEE Access, 2023 As wheat is one of the major crops worldwide, therefore, accurate disease detection in wheat plants is critical for mitigating effects and halting disease spread. Nowadays, the detection of diseases through images using machine learning and deep learning
Abdulaziz Alharbi +2 more
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Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions
, 2017 We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent ...
Katevas, Kleomenis +3 more
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Arthritis Care &Research, EarlyView.Objective A patient‐centered approach for chronic disease management, including systemic lupus erythematosus (SLE), aligns treatment with patients’ values and preferences, leading to improved outcomes. This paper summarizes how patient experiences, perspectives, and priorities informed the American College of Rheumatology (ACR) 2024 Lupus Nephritis (LN)
Shivani Garg +20 more
wiley +1 more source
Continual learning for predictive maintenance: Overview and challenges
Intelligent Systems with Applications, 2023 Deep learning techniques have become one of the main propellers for solving engineering problems effectively and efficiently. For instance, Predictive Maintenance methods have been used to improve predictions of when maintenance is needed on different ...
Julio Hurtado +4 more
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Open-world continual learning: Unifying novelty detection and continual learning
Artificial IntelligenceAs AI agents are increasingly used in the real open world with unknowns or novelties, they need the ability to (1) recognize objects that (a) they have learned before and (b) detect items that they have never seen or learned, and (2) learn the new items incrementally to become more and more knowledgeable and powerful. (1) is called novelty detection or
Gyuhak Kim +4 more
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Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Continual Contrastive Learning for Cross-Dataset Scene Classification
Remote Sensing, 2022 With the development of remote sensing technology, the continuing accumulation of remote sensing data has brought great challenges to the remote sensing field. Although multiple deep-learning-based classification methods have made great progress in scene
Rui Peng +4 more
doaj +1 more source