Symmetrization of multi-class losses produces a unique convex symmetric loss that locally approximates others and supports robust neural training under label noise.
5th International Conference on Learning Representations,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
LLQR+SAM pairs a slow learned geometry preconditioner with fast SAM perturbations to amplify escape from locally sharp 'potholes' while stabilizing flat basins, producing consistent gains over SAM and LLQR alone.
Forward replay replaces backward spreading in LLM parameter editing by optimizing the target hidden state at the first editing layer and propagating it forward, yielding more accurate layer-wise targets at the same computational cost.
citing papers explorer
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Symmetrization of Loss Functions for Robust Training of Neural Networks in the Presence of Noisy Labels
Symmetrization of multi-class losses produces a unique convex symmetric loss that locally approximates others and supports robust neural training under label noise.
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Navigating Potholes with Geometry-Aware Sharpness Minimization
LLQR+SAM pairs a slow learned geometry preconditioner with fast SAM perturbations to amplify escape from locally sharp 'potholes' while stabilizing flat basins, producing consistent gains over SAM and LLQR alone.
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From Backward Spreading to Forward Replay: Revisiting Target Construction in LLM Parameter Editing
Forward replay replaces backward spreading in LLM parameter editing by optimizing the target hidden state at the first editing layer and propagating it forward, yielding more accurate layer-wise targets at the same computational cost.