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New research details incremental learning in mirror flows

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv introduces a method for incremental learning within mirror flows. The research, authored by Raphaël Berthier, details how mirror flows generated by convex quadratic loss and a general convex lower semicontinuous mirror potential can converge to a limiting mirror flow. This convergence is achieved when initialized near the boundary of the mirror potential's domain, providing a general mechanism for incremental learning. AI

IMPACT Introduces a novel mechanism for incremental learning in mathematical models, potentially impacting future AI development.

RANK_REASON Academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research details incremental learning in mirror flows

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Academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Loucas Pillaud-Vivien ·

    Incremental Learning in Mirror Flows

    We study mirror flows generated by a convex quadratic loss and a general convex lower semicontinuous mirror potential. We show that, when initialized near the boundary of the domain of the mirror potential, their rescaled trajectories converge to a limiting mirror flow whose pote…

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