Summary of One Initialization to Rule Them All: Fine-tuning Via Explained Variance Adaptation, by Fabian Paischer et al.
One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptationby Fabian Paischer, Lukas Hauzenberger,…
One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptationby Fabian Paischer, Lukas Hauzenberger,…
Selective Aggregation for Low-Rank Adaptation in Federated Learningby Pengxin Guo, Shuang Zeng, Yanran Wang, Huijie…
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Experimental Evaluation of Machine Learning Models for Goal-oriented Customer Service Chatbot with Pipeline Architectureby Nurul…
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SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Valuesby Chengwei Sun, Jiwei Wei, Yujia…
VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generationby Yecheng Wu, Zhuoyang Zhang, Junyu…
Introduction to Machine Learningby Laurent YounesFirst submitted to arxiv on: 4 Sep 2024CategoriesMain: Machine Learning…
EMP: Enhance Memory in Data Pruningby Jinying Xiao, Ping Li, Jie Nie, Zhe TangFirst submitted…
Toward the Evaluation of Large Language Models Considering Score Variance across Instruction Templatesby Yusuke Sakai,…