Aurora is a leverage-aware spectral optimizer that enforces uniform row norms in matrix updates while preserving Muon's polar geometry, outperforming Muon and achieving SOTA among spectral methods on modded-nanoGPT.
Shashi Narayan, Shay B Cohen, and Mirella Lapata
3 Pith papers cite this work, alongside 81 external citations. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
AdaLoRA uses SVD-based pruning to allocate the parameter budget for low-rank fine-tuning updates according to per-matrix importance scores, yielding better performance than uniform allocation especially under tight budgets.
SCENIC framework reports up to 99% exact match on structured IoT command generation using sub-0.2B models, with pruned INT8 versions retaining 91% EM@1 after 25% size reduction.
citing papers explorer
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Aurora: A Leverage-Aware Spectral Optimizer
Aurora is a leverage-aware spectral optimizer that enforces uniform row norms in matrix updates while preserving Muon's polar geometry, outperforming Muon and achieving SOTA among spectral methods on modded-nanoGPT.
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AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
AdaLoRA uses SVD-based pruning to allocate the parameter budget for low-rank fine-tuning updates according to per-matrix importance scores, yielding better performance than uniform allocation especially under tight budgets.
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SCENIC: Semantic-Conditioned Edge-Aware Neural Framework for Structured IoT Command Generation
SCENIC framework reports up to 99% exact match on structured IoT command generation using sub-0.2B models, with pruned INT8 versions retaining 91% EM@1 after 25% size reduction.