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SUMMARY:Development of algorithms for partial multi-label machine learning (Webinar)
DESCRIPTION:Register here\n\n\n\n\n\n\n\n\nAbout this webinar\n\n\n\nMachine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Multi-label learning is a type of machine learning problem where each data instance can be associated with multiple labels simultaneously. Partial multi-label learning addresses problems where each instance is assigned a candidate label set and only a subset of these candidate labels is correct. Partial multi-label learning is particularly useful in scenarios where perfect labeling is expensive or impractical\, making it an essential area in weakly supervised learning; however\, a major issue of partial multi-label learning is that the training procedure can be easily misguided by noisy labels. \n\n\n\nIn this webinar\, we will talk about the general features of multiple partial multi-label methods\, and then the development of learning algorithms to handle dataset with large noisy labels across different domains using various frameworks\, with a focus on the recently developed methods for partial multi-label learning based on the Encoder-Decoder framework. \n\n\n\nSpeaker: Mengjie Han\, Associate Professor at Dalarna University. \n\n\n\nWho is this webinar for?\n\n\n\nThis webinar is suitable for data scientists\, software developers\, scientific researchers\, and AI practitioners who are: \n\n\n\n\nfamiliar with the basics of machine learning\n\n\n\nworking on multi-label optimization and learning\, and image processing\n\n\n\nalgorithm developers for scientific packages\n\n\n\n\nKey takeaways\n\n\n\nAfter attending this seminar\, you will: \n\n\n\n\nBe familiar with classic optimization problems\n\n\n\nGet to know an empirical application of Transformers\n\n\n\nUnderstand the importance of algorithms design\n\n\n\nBe aware of how speed enhancements are important to the results\n\n\n\n\nMore events & contact\n\n\n\nCheck out more upcoming events from ENCCS and our European network at https://enccs.se/events. \n\n\n\nFor questions regarding this workshop or general questions about ENNCS training events\, please contact training@enccs.se \n\n\n\nSchedules can change!\n\n\n\nTo ensure that everyone has the opportunity to participate\, we kindly request that you let us know as soon as possible if you are unable to attend an event after registering. \n\n\n\nPlease send us an email at training@enccs.se to cancel your attendance. \n\n\n\nWe understand things can change\, but repeated cancellations without notice may unfortunately result in your name being removed from future event registration lists. \n\n\n\n\n\n\n\nRegulations\n\n\n\nDue to EuroCC2 regulations\, we CAN NOT ACCEPT generic or private email addresses. Please use your official university or company email address for registration. \n\n\n\nThis training is for users who live and work in the European Union or a country associated with Horizon 2020. You can read more about the countries associated with Horizon2020 HERE.
URL:https://enccs.se/events/partial-multi-label-machine-learning-webinar/
CATEGORIES:ENCCS Event
ATTACH;FMTTYPE=image/webp:https://media.enccs.se/2025/02/webinar_yonglei.webp
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