AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Momentum as Residual-Driven Multiplier Correction for Deep Learning Optimization
An AIM framework based on residual-penalty variable splitting, which interprets momentum as a multiplier-like correction driven by the splitting residual, and RADAR, which combines relativistic adaptive geometry, decoupled residual correction, and second-order momentum filtering to improve the update direction and momentum estimation.