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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 3121–3130 of 9051 papers

TitleStatusHype
DiffusionPhase: Motion Diffusion in Frequency Domain—0
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors—0
Generalization to New Sequential Decision Making Tasks with In-Context Learning—0
On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation ParadigmCode1
Seller-side Outcome Fairness in Online Marketplaces—0
Mitigating Open-Vocabulary Caption HallucinationsCode1
FoodFusion: A Latent Diffusion Model for Realistic Food Image Generation—0
Enhancing Molecular Property Prediction via Mixture of Collaborative ExpertsCode0
On the Role of Edge Dependency in Graph Generative Models—0
SO-NeRF: Active View Planning for NeRF using Surrogate Objectives—0
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