298 papers with code • 0 benchmarks • 13 datasets
Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model
modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler.
In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered.
We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature.
Ranked #6 on Few-Shot Image Classification on Meta-Dataset Rank
We study the application of active learning techniques to the translation of unbounded data streams via interactive neural machine translation.
Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning.