Jul 29, 2021 � In this work, we analyze an efficient active learning algorithm, which focuses on the large batch setting.
In this paper, we develop, analyze, and evaluate a batch active learning algorithm called Cluster-. Margin, which we show can scale to batch sizes of 100K or�...
Nov 9, 2021 � We develop a batch active learning approach that is effective for very large batch sizes (100K-1M).
Jun 10, 2024 � Batch active learning, which adaptively issues batched queries to a labeling oracle, is a common approach for addressing this problem. The�...
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Batch active learning, which adaptively issuesbatched queries to a labeling oracle, is a common approach for addressing thisproblem. The�...
Jun 9, 2022 � In this post, I explain Batch Mode Active Learning (BMAL) concept and introduce you some BMAL methods which are proposed to overcome the drawbacks of�...
While naive batch construction methods result in correlated queries, our algorithm produces diverse batches that enable efficient active learning at scale.
The batch mode of active learning is one where labels are queried in batches of suitable size, and the models are re-trained/updated either after each batch or�...
An implementation of the state-of-the-art Deep Active Learning algorithm. This code was built based on Jordan Ash's repository.
Mar 17, 2022 � We study active learning methods that adaptively select batches of unlabeled data for labeling. We present a framework for constructing such methods.
