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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 981990 of 9051 papers

TitleStatusHype
Progressive Multimodal Reasoning via Active Retrieval0
HSEvo: Elevating Automatic Heuristic Design with Diversity-Driven Harmony Search and Genetic Algorithm Using LLMsCode1
Inferring protein folding mechanisms from natural sequence diversity0
Uncertainty Awareness in Wireless Communications and Sensing0
Socio-Culturally Aware Evaluation Framework for LLM-Based Content Moderation0
Generating Diverse Hypotheses for Inductive Reasoning0
Hybrid CNN-LSTM based Indoor Pedestrian Localization with CSI Fingerprint MapsCode1
AnySat: One Earth Observation Model for Many Resolutions, Scales, and ModalitiesCode2
A Unifying Information-theoretic Perspective on Evaluating Generative Models0
Embedding Cultural Diversity in Prototype-based Recommender Systems0
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