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Related Questions
- What are the key differences between query-by-committee and core-set selection in terms of handling uncertainty and noise in dataset?
- Can you explain how query-by-committee and core-set selection approach the problem of selecting representative samples from high-dimensional data?
- How do query-by-committee and core-set selection handle the trade-off between exploration and exploitation during the sampling process?
- What are some common applications where query-by-committee and core-set selection are used, such as in active learning, transfer learning, or deep learning?
- Can you provide a contrastive analysis of query-by-committee and core-set selection in terms of their convergence rates, computational complexity, and scalability?
- How do query-by-committee and core-set selection handle the issue of sample selection bias and estimation bias in the sampling process?
- Are there any known scenarios or datasets where one approach has been shown to outperform the other, and vice versa?
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