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Research questionHow can scientific machine-learning models overcome optimization plateaus when their local linearized subspace permits greater accuracy?Gradient-based training can stop well above the error level available within a model’s local linearized subspace. Ill-conditioning can preserve this gap even when the resulting optimization problem is convex, making the source of the plateau difficult to distinguish from a lack of model capacity.
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Latest papersRecent research connected to this question, newest first.Linearized subspace refinement framework to expose hidden accuracy in trained neural networksThe evidence concerns architecture-agnostic post-training refinement of trained neural networks using their local linearized models. Reported settings include function approximation, data-driven operator learning, physics-informed operator fine-tuning, and noisy inverse problems; the findings indicate that subspace rank affects correction strength, numerical stability, and noise sensitivity.research paper · Sep 3, 2026
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