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• W2948367246 abstract "We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both predictive uncertainty and sample diversity into every selected batch. Crucially, BADGE trades off between diversity and uncertainty without requiring any hand-tuned hyperparameters. We show that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems." @default.
• W2948367246 created "2019-06-14" @default.
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• W2948367246 date "2019-06-09" @default.
• W2948367246 modified "2023-09-27" @default.
• W2948367246 title "Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds" @default.
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