In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.
Sharp Generalization Bounds for Foundation Models with Asymmetric Ran- domized Low-Rank Adapters
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
MM-SOLD is a training-free particle sampler whose large-particle limit converges to a moment-matched Gibbs distribution obtained by exponentially tilting a score-smoothed target.
Converts impossibility theorems into architecture-dependent accuracy ceilings and design rules for transformers and other AI subfields, with the Deterministic Horizon measured at 19-31 across twelve models.
In linear regression, LoRA can achieve lower excess risk than full fine-tuning when the pretraining-downstream difference is low-rank, and small LoRA ranks can improve generalization by acting as regularization.
citing papers explorer
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High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model
In a solvable attention model, pre-training followed by rank-one LoRA admits sharp asymptotic predictions for test errors and representation alignment via an effective noise term.
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Training-Free Generative Sampling via Moment-Matched Score Smoothing
MM-SOLD is a training-free particle sampler whose large-particle limit converges to a moment-matched Gibbs distribution obtained by exponentially tilting a score-smoothed target.
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The Deterministic Horizon: Impossibility Results as Design Specifications for Trustworthy AI Systems
Converts impossibility theorems into architecture-dependent accuracy ceilings and design rules for transformers and other AI subfields, with the Deterministic Horizon measured at 19-31 across twelve models.
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LoRA vs. Full Fine-Tuning: A Theoretical Perspective
In linear regression, LoRA can achieve lower excess risk than full fine-tuning when the pretraining-downstream difference is low-rank, and small LoRA ranks can improve generalization by acting as regularization.