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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 2141–2150 of 9051 papers

TitleStatusHype
Curating Grounded Synthetic Data with Global Perspectives for Equitable AI—0
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems—0
Learning Continually by Spectral Regularization—0
mHuBERT-147: A Compact Multilingual HuBERT ModelCode0
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark—0
UnSupDLA: Towards Unsupervised Document Layout Analysis—0
Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-TrainingCode1
Data Augmentation in Earth Observation: A Diffusion Model Approach—0
Optimisation of federated learning settings under statistical heterogeneity variations—0
An Efficient Framework for Crediting Data Contributors of Diffusion Models—0
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