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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 26412650 of 9051 papers

TitleStatusHype
First the worst: Finding better gender translations during beam searchCode0
FireFly A Synthetic Dataset for Ember Detection in WildfireCode0
First U-Net Layers Contain More Domain Specific Information Than The Last OnesCode0
Behind Recommender Systems: the Geography of the ACM RecSys CommunityCode0
Finer Metagenomic Reconstruction via Biodiversity OptimizationCode0
Flickr-PAD: New Face High-Resolution Presentation Attack Detection DatabaseCode0
Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in HanabiCode0
DESTEIN: Navigating Detoxification of Language Models via Universal Steering Pairs and Head-wise Activation FusionCode0
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text GenerationCode0
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAGCode0
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