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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This paper proposes parameter-efficient fine-tuning methods for protein language models to democratize their application, reducing computational costs while maintaining performance.
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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 proposes parameter-efficient fine-tuning (PEFT) methods for geospatial foundation models, enabling effective adaptation with minimal parameter updates.
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DiffFit proposes a simple parameter-efficient fine-tuning strategy for large diffusion models, enabling transferability with minimal trainable parameters.
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This paper introduces SCT, a simple yet effective parameter-efficient fine-tuning method for vision transformers that selectively updates salient channels based on feature map statistics.
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Proposes a flexible, parameter-efficient fine-tuning scheme for pretrained architectures to improve generalization across tasks.
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This paper provides a comprehensive analysis of parameter-efficient fine-tuning methods across diverse applications including reasoning, video description, and more.
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ADAPTERX is a general parameter-efficient fine-tuning framework where different blocks recursively select the most suitable adapters for vision tasks.
Dan Zhang, Tao Feng, Lilong Xue, et al.
This survey provides a comprehensive overview of Parameter-Efficient Fine-Tuning (PEFT) methods for foundation models, categorizing and analyzing approaches to reduce computational costs while maintaining performance.
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LongLoRA enables efficient context extension for large language models via sparse local attention and parameter-efficient fine-tuning.
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DyLoRA introduces a dynamic, search-free low-rank adaptation method that trains LoRA blocks for a range of ranks to improve parameter-efficient tuning of pre-trained models.
Baohao Liao, Yan Meng, C. Monz
This paper proposes a parameter-efficient fine-tuning method that matches full fine-tuning performance without introducing additional inference latency.