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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 3121–3130 of 9051 papers

TitleStatusHype
Diversity and Diffusion: Observations on Synthetic Image Distributions with Stable Diffusion—0
Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption—0
Energy-Efficient Design of Broad Beams for Massive MIMO Systems—0
Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples—0
Affinity-Preserving Random Walk for Multi-Document Summarization—0
Enforcing Structural Diversity in Cube-pruned Dependency Parsing—0
Diversity and Depth in Per-Example Routing Models—0
Engineering Artificial Intelligence: Framework, Challenges, and Future Direction—0
English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech Recognition System—0
Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models—0
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