WebApr 8, 2024 · Exploring Data Geometry for Continual Learning. Zhi Gao, Chen Xu, Feng Li, Yunde Jia, Mehrtash Harandi, Yuwei Wu. Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. WebAbstract. Stationary frames in the Kerr geometry are rewieved and their properties compared. In particular, the locally non-rotating frame, the Carter frame, the static frame and the frame connected with the equatorial circular geodesic are treated as the most important examples. Download to read the full article text.
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Exploring Data Geometry for Continual Learning - Semantic Scholar
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