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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 21512160 of 9051 papers

TitleStatusHype
Robust portfolio optimization for recommender systems considering uncertainty of estimated statistics0
MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story GenerationCode1
Contrastive Learning from Synthetic Audio Doppelgängers0
Flow of Reasoning:Training LLMs for Divergent Problem Solving with Minimal ExamplesCode2
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models0
Diverse 3D Human Pose Generation in Scenes based on Decoupled Structure0
Baking Symmetry into GFlowNets0
Creativity Has Left the Chat: The Price of Debiasing Language Models0
Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language ModelsCode0
Select-Mosaic: Data Augmentation Method for Dense Small Object ScenesCode0
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