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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 27412750 of 9051 papers

TitleStatusHype
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models0
TempCompass: Do Video LLMs Really Understand Videos?Code2
AI and Identity0
Team Formation amidst ConflictsCode0
A minimal model of pan-immunity maintenance by horizontal gene transfer in the ecological dynamics of bacteria and phages0
Teaching Large Language Models an Unseen Language on the FlyCode1
DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments0
Enhancing the "Immunity" of Mixture-of-Experts Networks for Adversarial Defense0
Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data SilosCode0
WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image SynthesisCode2
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