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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 5281–5290 of 9051 papers

TitleStatusHype
Similarity of Information and Collective Action—0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
Simon @ LT-EDI-EACL2021: Detecting Hope Speech with BERT—0
Simple Regularisation for Uncertainty-Aware Knowledge Distillation—0
SimpleStrat: Diversifying Language Model Generation with Stratification—0
Simultaneously Learning Architectures and Features of Deep Neural Networks—0
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport—0
Simultaneous Relevance and Diversity: A New Recommendation Inference Approach—0
SimVQA: Exploring Simulated Environments for Visual Question Answering—0
Since the Scientific Literature Is Multilingual, Our Models Should Be Too—0
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