SGD is reformulated via a master equation from discrete updates, producing a discrete Fokker-Planck equation that predicts non-stationary variance growth proportional to learning rate in flat Hessian directions.
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SDE-based analysis of multi-task RRG gradient dynamics reveals a double dilemma, addressed by the backbone-agnostic CAME-Grad optimizer that improves clinical metrics by 2.3% on MIMIC-CXR and 1.9% on IU X-Ray across eight methods.
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Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics
SGD is reformulated via a master equation from discrete updates, producing a discrete Fokker-Planck equation that predicts non-stationary variance growth proportional to learning rate in flat Hessian directions.
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The Double Dilemma in Multi-Task Radiology Report Generation: A Gradient Dynamics Analysis and Solution
SDE-based analysis of multi-task RRG gradient dynamics reveals a double dilemma, addressed by the backbone-agnostic CAME-Grad optimizer that improves clinical metrics by 2.3% on MIMIC-CXR and 1.9% on IU X-Ray across eight methods.