1. Look, Listen and Learn
2. David Arthur and Sergei Vassilvitskii . 2007 . k-means: the advantages of careful seeding . In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007 , New Orleans, Louisiana, USA , January 7-9, 2007. SIAM, 1027--1035. http://dl.acm.org/citation.cfm?id=1283383.1283494 David Arthur and Sergei Vassilvitskii. 2007. k-means: the advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007, New Orleans, Louisiana, USA, January 7-9, 2007. SIAM, 1027--1035. http://dl.acm.org/citation.cfm?id=1283383.1283494
3. Jordan T. Ash , Chicheng Zhang , Akshay Krishnamurthy , John Langford , and Alekh Agarwal . 2020 . Deep Batch Active Learning by Diverse , Uncertain Gradient Lower Bounds. In 8th International Conference on Learning Representations, ICLR 2020 , Addis Ababa, Ethiopia , April 26-30, 2020. OpenReview.net. https://openreview.net/forum?id=ryghZJBKPS Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal. 2020. Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https://openreview.net/forum?id=ryghZJBKPS
4. Multimodal Machine Learning: A Survey and Taxonomy
5. The Power of Ensembles for Active Learning in Image Classification