ContinualAI RG: "Understanding Continual Learning Settings with Data Distribution Drift Analysis" скачать в хорошем качестве

ContinualAI RG: "Understanding Continual Learning Settings with Data Distribution Drift Analysis"

This Friday 23-04-2021, 5.30pm CEST, for the ContinualAI Reading Group, Timothée Lesort (MILA) presented the paper: Title: “Understanding Continual Learning Settings with Data Distribution Drift Analysis” Abstract: Classical machine learning algorithms often assume that the data are drawn i.i.d. from a stationary probability distribution. Recently, continual learning emerged as a rapidly growing area of machine learning where this assumption is relaxed, namely, where the data distribution is non-stationary, i.e., changes over time. However, data distribution drifts may interfere with the learning process and erase previously learned knowledge; thus, continual learning algorithms must include specialized mechanisms to deal with such distribution drifts. A distribution drift may change the class labels distribution, the input distribution, or both. Moreover, distribution drifts might be abrupt or gradual. In this paper, we aim to identify and categorize different types of data distribution drifts and potential assumptions about them, to better characterize various continual-learning scenarios. Moreover, we propose to use the distribution drift framework to provide more precise definitions of several terms commonly used in the continual learning field. The event was moderated by: Vincenzo Lomonaco. Check out our forum thread to learn more: https://continualai.discourse.group/t... ---------------- ContinualAI is an official non-profit research organization and the largest open community on Continual Learning for AI. We aim at connecting people and working better together on this fascinating topic we consider fundamental for the future of AI. Join us for the next Online Meetup! • Official website: https://www.continualai.org​ • Join us now at https://www.continualai.org/join_us​ Please consider supporting us with a small donation at: https://www.continualai.org/supporters​ It's thanks to people like you that we are making ContinualAI a reality!