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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

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Showing 81–90 of 9051 papers

TitleStatusHype
Exploration by Random Reward Perturbation—0
Diffusion Models for Safety Validation of Autonomous Driving Systems—0
Diversity-Guided MLP Reduction for Efficient Large Vision TransformersCode1
Slow and Fast Neurons Cooperate in Contextual Working Memory through Timescale Diversity—0
SoK: Data Reconstruction Attacks Against Machine Learning Models: Definition, Metrics, and Benchmark—0
A weighted quantum ensemble of homogeneous quantum classifiers—0
OpenDance: Multimodal Controllable 3D Dance Generation Using Large-scale Internet Data—0
Correlated Errors in Large Language Models—0
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning—0
REMoH: A Reflective Evolution of Multi-objective Heuristics approach via Large Language Models—0
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