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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 11511160 of 9051 papers

TitleStatusHype
A View From Somewhere: Human-Centric Face RepresentationsCode1
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental SegmentationCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
New Protocols and Negative Results for Textual Entailment Data CollectionCode1
Color Space Learning for Cross-Color Person Re-IdentificationCode1
COMETA: A Corpus for Medical Entity Linking in the Social MediaCode1
BoostTree and BoostForest for Ensemble LearningCode1
Combating noisy labels by agreement: A joint training method with co-regularizationCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption GenerationCode1
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