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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

Showing 30213030 of 9051 papers

TitleStatusHype
Dataset Clustering for Improved Offline Policy LearningCode0
Evaluating Fairness in Argument RetrievalCode0
Exact Fusion via Feature Distribution Matching for Few-shot Image GenerationCode0
Discovering Representations for Black-box OptimizationCode0
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
Probing Simile Knowledge from Pre-trained Language ModelsCode0
Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question AnsweringCode0
EuLearn: A 3D database for learning Euler characteristicsCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior AnalysisCode0
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