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Related Questions
- What are the key differences between negative sampling strategies such as uniform, binary, and hierarchical sampling in self-supervised learning?
- How does the choice of negative sampling strategy impact the quality of representations learned by self-supervised models?
- Can you explain the trade-offs between different negative sampling strategies in terms of computational efficiency and representation learning capacity?
- How does the negative sampling strategy affect the ability of self-supervised models to capture long-range dependencies in data?
- What are the implications of using different negative sampling strategies for downstream tasks such as classification and clustering?
- Can you discuss the role of negative sampling strategy in self-supervised learning for multimodal data such as images and text?
- How does the choice of negative sampling strategy impact the stability and convergence of self-supervised learning algorithms?
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