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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 30313040 of 9051 papers

TitleStatusHype
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
Evaluating Coherence in Dialogue Systems using EntailmentCode0
ETS: Efficient Tree Search for Inference-Time ScalingCode0
Promoting Fairness and Diversity in Speech Datasets for Mental Health and Neurological Disorders ResearchCode0
Automatic Synthesis of Diverse Weak Supervision Sources for Behavior AnalysisCode0
EuLearn: A 3D database for learning Euler characteristicsCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series ForecastingCode0
Ethical Considerations for Responsible Data CurationCode0
Evaluating Creative Short Story Generation in Humans and Large Language ModelsCode0
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