Summary of Regal: Python Package For Active Learning Of Regression Problems, by Elizaveta Surzhikova and Jonny Proppe
regAL: Python Package for Active Learning of Regression Problemsby Elizaveta Surzhikova, Jonny ProppeFirst submitted to…
regAL: Python Package for Active Learning of Regression Problemsby Elizaveta Surzhikova, Jonny ProppeFirst submitted to…
AutoAL: Automated Active Learning with Differentiable Query Strategy Searchby Yifeng Wang, Xueying Zhan, Siyu HuangFirst…
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignmentby Weishan Cai,…
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learningby Peter Eckmann, Dongxia Wu, Germano…
ALVIN: Active Learning Via INterpolationby Michalis Korakakis, Andreas Vlachos, Adrian WellerFirst submitted to arxiv on:…
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequencesby Danny Y. C. Wang, Lars Arne…
MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learningby Sofya…
Language Model-Driven Data Pruning Enables Efficient Active Learningby Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha…
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model…
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planesby Erica Zhang, Fangzhao Zhang, Mert…