A survey proposes a novel 3D taxonomy classifying drifts into time stream, data stream, and model stream categories to unify research on non-stationary autonomous learning.
Measuring domain shift for deep learning in histopathology,
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Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.
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Autonomous Drift Learning in Data Streams: A Unified Perspective
A survey proposes a novel 3D taxonomy classifying drifts into time stream, data stream, and model stream categories to unify research on non-stationary autonomous learning.
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Decentralized Learning via Random Walk with Jumps
Metropolis-Hastings with Levy jumps prevents entrapment in weighted random walks, yielding a convergence rate that accounts for data heterogeneity, network spectral gap, and jump probability.