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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 611620 of 9051 papers

TitleStatusHype
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion ProcessCode1
Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster MemoryCode1
Ensemble Diversity Facilitates Adversarial TransferabilityCode1
HomoFormer: Homogenized Transformer for Image Shadow RemovalCode1
ODAQ: Open Dataset of Audio QualityCode1
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3DCode1
HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs ResponsesCode1
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide GenerationCode1
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