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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
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline GenerationCode1
Diverse Cotraining Makes Strong Semi-Supervised SegmentorCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
Diverse Image Generation via Self-Conditioned GANsCode1
Diverse Image-to-Image Translation via Disentangled RepresentationsCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Diverse Semantic Image Synthesis via Probability Distribution ModelingCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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