A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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The paper proposes a dynamic low-rank sparse adaptation method that unifies LLM sparsity and low-rank adaptation for efficient fine-tuning.
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RidgeloRA enhances LoRA by incorporating matrix ridge regression to improve parameter-efficient fine-tuning of large language models.
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LoRA-XS introduces a low-rank adaptation method that drastically reduces trainable parameters while maintaining performance.
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The paper introduces nested low-rank adaptation, a method that reduces the number of trainable parameters for fine-tuning large models compared to standard LoRA.
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This study presents a low-rank adaptation method for finetuning large language models, embedding a semantic-aware low-rank adaptation module into attention layers.
Shangao Li, Yao Zhang, Volker Tresp, et al.
QuoteBench reveals that matched execution scores hide command-path failures in LLM coding agents, showing that boundary adaptation, not raw generation, separates models.
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This paper introduces in-context fine-tuning for time-series foundation models, enabling adaptation to new datasets without updating model weights.
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This paper introduces a sim-to-real approach using randomized-to-canonical adaptation networks to transfer a vision-based closed-loop grasping reinforcement learning agent from simulation to a real robot.
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This paper proposes Softmax-DPO, a novel adaptation of Direct Preference Optimization for recommendation systems, integrating softmax loss to enhance implicit feedback modeling.
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This survey systematically reviews Low-Rank Adaptation (LoRA) methods and their variants for Large Language Models (LLMs), emphasizing their efficiency in adapting models without full fine-tuning.
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Introduces Correlated Low-Rank Adaptation (CoLoRA), a novel framework that explicitly models correlations among low-rank factors to improve parameter-efficient fine-tuning of convolutional networks.
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This paper introduces Lora-fa, a memory-efficient low-rank adaptation method that reduces the memory footprint of fine-tuning large language models by avoiding the need to store full-rank gradients.