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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 28812890 of 9051 papers

TitleStatusHype
Exploring Flat Minima for Domain Generalization with Large Learning RatesCode0
Exploring Diversity in Back Translation for Low-Resource Machine TranslationCode0
Exploring Format Consistency for Instruction TuningCode0
DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant RecommendationsCode0
CatVRNN: Generating Category Texts via Multi-task LearningCode0
Exploring Precision and Recall to assess the quality and diversity of LLMsCode0
Exploratory State Representation LearningCode0
Exploiting ConvNet Diversity for Flooding IdentificationCode0
CausalDialogue: Modeling Utterance-level Causality in ConversationsCode0
Contributions of El Niño Southern Oscillation (ENSO) Diversity to Low-Frequency Changes in ENSO VarianceCode0
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