BAL: Balancing Diversity and Novelty for Active Learning

26 December 2023

Papers citing "BAL: Balancing Diversity and Novelty for Active Learning"

6 / 6 papers shown

Title
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts Yunxin Li Shenyuan Jiang Baotian Hu Longyue Wang Wanqi Zhong Wenhan Luo Lin Ma Min-Ling Zhang MoE 39 28 0 18 May 2024
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm Yichen Xie Han Lu Junchi Yan Xiaokang Yang M. Tomizuka Wei Zhan 38 30 0 25 Mar 2023
Efficient Bayesian Updates for Deep Learning via Laplace Approximations Denis Huseljic M. Herde Lukas Rauch Paul Hahn Zhixin Huang D. Kottke S. Vogt Bernhard Sick BDL 16 0 0 12 Oct 2022
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets Guy Hacohen Avihu Dekel D. Weinshall 124 116 0 06 Feb 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 305 7,434 0 11 Nov 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015