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Found 8 papers, 2 papers with code
Domain-Aware Augmentations for Unsupervised Online General Continual Learning
Continual Learning has been challenging, especially when dealing with unsupervised scenarios such as Unsupervised Online General Continual Learning (UOGCL), where the learning agent has no prior knowledge of class boundaries or task change information.
New metrics for analyzing continual learners
This scenario, known as Continual Learning (CL) poses challenges to standard learning algorithms which struggle to maintain knowledge of old tasks while learning new ones.
Learning Representations on the Unit Sphere: Investigating Angular Gaussian and von Mises-Fisher Distributions for Online Continual Learning
We propose to use the angular Gaussian distribution, which corresponds to a Gaussian projected on the unit-sphere and derive the associated loss function.
Low Complexity Approaches for End-to-End Latency Prediction
Software Defined Networks have opened the door to statistical and AI-based techniques to improve efficiency of networking.
Low Complexity Adaptive Machine Learning Approaches for End-to-End Latency Prediction
In this paper, we improve our previously proposed low-cost estimators [6] by adding the adaptive dimension, and show that the performances are minimally modified while gaining the ability to track varying networks.
Contrastive Learning for Online Semi-Supervised General Continual Learning
We study Online Continual Learning with missing labels and propose SemiCon, a new contrastive loss designed for partly labeled data.
On some interrelations of generalized $q$-entropies and a generalized Fisher information, including a Cramér-Rao inequality
The Cram\'er-Rao inequality shows that the generalized $q$-Gaussians also minimize the generalized Fisher information among distributions with a fixed moment.
Some results on a $χ$-divergence, an~extended~Fisher information and~generalized~Cramér-Rao inequalities
We propose a modified $\chi^{\beta}$-divergence, give some of its properties, and show that this leads to the definition of a generalized Fisher information.
