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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 191200 of 9051 papers

TitleStatusHype
DiffVLA: Vision-Language Guided Diffusion Planning for Autonomous Driving0
Pan-tropical plant functional trait variation from space0
PIGPVAE: Physics-Informed Gaussian Process Variational Autoencoders0
MMP-2K: A Benchmark Multi-Labeled Macro Photography Image Quality Assessment DatabaseCode1
Less is More: Efficient Point Cloud Reconstruction via Multi-Head Decoders0
The Price of Format: Diversity Collapse in LLMsCode0
MGD^3: Mode-Guided Dataset Distillation using Diffusion Models0
Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions0
SpokenNativQA: Multilingual Everyday Spoken Queries for LLMs0
Voice of a Continent: Mapping Africa's Speech Technology Frontier0
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