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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 26212630 of 9051 papers

TitleStatusHype
ThemeStation: Generating Theme-Aware 3D Assets from Few ExemplarsCode3
An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-IdentificationCode0
UniTraj: A Unified Framework for Scalable Vehicle Trajectory PredictionCode3
Protected group bias and stereotypes in Large Language Models0
Can 3D Vision-Language Models Truly Understand Natural Language?Code1
An Analysis of the Preferences of Distribution Indicators in Evolutionary Multi-Objective Optimization0
Genetic diversity of barley accessions and their response under abiotic stresses using different approaches0
LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion ModelsCode0
Improving the Robustness of Large Language Models via Consistency Alignment0
A reinforcement learning guided hybrid evolutionary algorithm for the latency location routing problemCode0
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