✳flâneur — a map of the web's best reading
Learning with not Enough Data Part 2: Active Learning
lilianweng.github.io · 4 words · saved by 1 readers
The performance of supervised learning tasks improves with more high-quality labels available. However, it is expensive to collect a large number of labeled samples. Active learning is one paradigm to deal with not enough labeled data, when there are resources for labeling more data samples but under a limited budget....
← Semi Supervised Learning
Explore this link on the map →related reading
- Learning with not Enough Data Part 1: Semi-Supervised Learning | Lil'Loglilianweng.github.io
- Semi-Supervised Learning: Techniques & Examples [2024]v7labs.com
- Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study | Soft Computing and Intelligent Information Systemssci2s.ugr.es
- What Is Semi-Supervised Learning - MachineLearningMastery.commachinelearningmastery.com
- Growing or Compressing Datasets · Introduction to Data-Centric AIdcai.csail.mit.edu
- Learning to be Bayesian without Supervisionpapers.nips.cc
- Thinking about High-Quality Human Data | Lil'Loglilianweng.github.io
- Why you need to improve your training data, and how to do it << Pete Warden's blogpetewarden.com
- Big Self-Supervised Models are Strong Semi-Supervised Learnersarxiv.org
- deeplearningbook.org/contents/ml.htmldeeplearningbook.org
- DataRater: Meta-Learned Dataset Curationarxiv.org
- MAI-Thinking-1: Building a Hill-Climbing Machinemicrosoft.ai