Tag: Few-Shot Learning
All the articles with the tag "Few-Shot Learning".
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Rethinking Meta-Learning from a Learning Lens
Published: at 11:18 AM
76.64 🤔This paper rethinks meta-learning from a 'learning' lens, proposing TRLearner, a plug-and-play method that leverages task relations to calibrate optimization, demonstrating significant performance improvements across regression, classification, drug activity, pose prediction, and OOD generalization tasks.
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MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models
Published: at 11:16 AM 73.67 🤔本文提出MMRL及MMRL++框架，通过共享表示空间和解耦策略增强视觉-语言模型的少样本适配能力，并利用参数高效的SRRA和PRC机制提升泛化性和训练稳定性，在多个数据集上取得最优性能。
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The dynamic interplay between in-context and in-weight learning in humans and neural networks
Published: at 11:20 PM 70.07 🤔本文通过神经网络中上下文学习（ICL）与权重学习（IWL）的动态交互，统一解释了人类学习中的组合性泛化、课程效应及灵活性与保留性权衡，为认知科学双过程理论提供了新视角。
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HyPerAlign: Hypotheses-driven Personalized Alignment
Published: at 04:32 PM 65.01 🤔本文提出HyPerAlign方法，通过假设驱动的少样本学习实现LLM的个性化对齐，提高了模型对个体用户的适应性和安全性，同时减少了对微调的依赖。
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How do Humans and Language Models Reason About Creativity? A Comparative Analysis
Published: at 10:59 AM 60.58 🤔This paper conducts a comparative analysis of creativity evaluation in STEM, revealing that human experts and LLMs prioritize different facets of originality (cleverness vs. remoteness/uncommonness) and are differentially influenced by contextual examples, with LLMs showing higher predictive accuracy but poorer construct validity due to homogenized facet correlations.