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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 42714280 of 9051 papers

TitleStatusHype
SemEval-2023 Task 10: Explainable Detection of Online SexismCode1
Controlled Diversity with Preference : Towards Learning a Diverse Set of Desired SkillsCode0
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy PerceptionCode2
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRICode1
Lformer: Text-to-Image Generation with L-shape Block Parallel Decoding0
Emergent competition shapes the ecological properties of multi-trophic ecosystems0
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer LearningCode0
Streaming Active Learning with Deep Neural NetworksCode2
SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic DataCode1
Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization0
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