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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 27612770 of 9051 papers

TitleStatusHype
Fast and Practical Neural Architecture SearchCode0
Deep Surrogate Assisted MAP-Elites for Automated Hearthstone DeckbuildingCode0
Batched Large-scale Bayesian Optimization in High-dimensional SpacesCode0
Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary EvaluationCode0
Camera Style Adaptation for Person Re-identificationCode0
Federated Neural Topic ModelsCode0
FairER: Entity Resolution with Fairness ConstraintsCode0
Hypergraph Clustering for Finding Diverse and Experienced GroupsCode0
Diversity Networks: Neural Network Compression Using Determinantal Point ProcessesCode0
Batch Decorrelation for Active Metric LearningCode0
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