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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 911920 of 9051 papers

TitleStatusHype
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue DatasetCode1
Self-Supervised Correspondence Estimation via Multiview RegistrationCode1
On the effectiveness of partial variance reduction in federated learning with heterogeneous dataCode1
UDE: A Unified Driving Engine for Human Motion GenerationCode1
VLTinT: Visual-Linguistic Transformer-in-Transformer for Coherent Video Paragraph CaptioningCode1
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image SegmentationCode1
Generative Modeling in Structural-Hankel Domain for Color Image InpaintingCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
SciRepEval: A Multi-Format Benchmark for Scientific Document RepresentationsCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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